{
 "acc0_abs_max_g": {
  "value": 34.156701587562154,
  "unit": "g",
  "estimand": {
   "population": "acc.csv files",
   "filter": "Acc0, all samples",
   "statistic": "largest absolute value, mm/s2 divided by 9806.65"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "acc0_mean_max_abs": {
  "value": 485.29953024669817,
  "unit": "mm/s2",
  "estimand": {
   "population": "acc.csv files",
   "filter": "Acc0",
   "statistic": "largest absolute file mean (DC offset)"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "acc1_abs_max_g": {
  "value": 23.03068386568018,
  "unit": "g",
  "estimand": {
   "population": "acc.csv files",
   "filter": "Acc1, all samples",
   "statistic": "largest absolute value, mm/s2 divided by 9806.65"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "acc_cols_max": {
  "value": 3,
  "unit": "columns",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "acc.csv files (37)",
   "statistic": "most columns"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "acc_cols_min": {
  "value": 3,
  "unit": "columns",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "acc.csv files (37)",
   "statistic": "fewest columns"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "acc_file_max_bytes": {
  "value": 1671981433,
  "unit": "bytes",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "acc.csv files",
   "statistic": "max size"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "acc_fs_est_max": {
  "value": 51200.000000046566,
  "unit": "Hz",
  "estimand": {
   "population": "acc.csv files",
   "filter": "all",
   "statistic": "max of 1/median time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "acc_fs_est_min": {
  "value": 51199.99998956919,
  "unit": "Hz",
  "estimand": {
   "population": "acc.csv files",
   "filter": "all",
   "statistic": "min of 1/median time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "acc_n_dt_off_total": {
  "value": 0,
  "unit": "steps",
  "estimand": {
   "population": "acc.csv files",
   "filter": "all",
   "statistic": "time steps differing from the file median by more than 1e-9 s"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "acc_nan_total": {
  "value": 0,
  "unit": "cells",
  "estimand": {
   "population": "acc.csv files",
   "filter": "all",
   "statistic": "missing cells"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "acc_rows_total": {
  "value": 603196337,
  "unit": "rows",
  "estimand": {
   "population": "acc.csv files",
   "filter": "all",
   "statistic": "rows"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "boot_b": {
  "value": 4000,
  "unit": "resamples",
  "estimand": {
   "population": "bootstrap setting",
   "filter": "all intervals in p03_stats.py",
   "statistic": "number of run-level resamples"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "bytes_acc": {
  "value": 32652898979,
  "unit": "bytes",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "acc.csv files",
   "statistic": "sum of sizes"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "bytes_match_kaggle": {
  "value": 1,
  "unit": "boolean",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "none",
   "statistic": "1 if the byte total equals Kaggle totalBytes"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "bytes_total": {
  "value": 40412136430,
  "unit": "bytes",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "none",
   "statistic": "sum of file sizes"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "bytes_wav": {
  "value": 6804111372,
  "unit": "bytes",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "WAV files",
   "statistic": "sum of sizes"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "ci_level": {
  "value": 0.95,
  "unit": "fraction",
  "estimand": {
   "population": "bootstrap setting",
   "filter": "all intervals in p03_stats.py",
   "statistic": "coverage level of the percentile intervals"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "conf_gate": {
  "value": 0.7,
  "unit": "probability",
  "estimand": {
   "population": "analyst judgment",
   "filter": "confidence that continuing past the data-quality gate is right",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "conf_if_confirmed": {
  "value": 0.7,
  "unit": "probability",
  "estimand": {
   "population": "analyst judgment",
   "filter": "confidence after the stated checks come back clean",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "conf_recommendation": {
  "value": 0.6,
  "unit": "probability",
  "estimand": {
   "population": "analyst judgment",
   "filter": "confidence in the recommendation",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "ct_cols_max": {
  "value": 2,
  "unit": "columns",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "ct.csv files (13)",
   "statistic": "most columns"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "ct_cols_min": {
  "value": 2,
  "unit": "columns",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "ct.csv files (13)",
   "statistic": "fewest columns"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "ct_dt_median_max": {
  "value": 10.0,
  "unit": "s",
  "estimand": {
   "population": "ct.csv files",
   "filter": "tmf_vf10",
   "statistic": "max over runs of the median time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "ct_dt_median_min": {
  "value": 10.0,
  "unit": "s",
  "estimand": {
   "population": "ct.csv files",
   "filter": "tmf_vf10",
   "statistic": "min over runs of the median time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "ct_rows_total": {
  "value": 1203,
  "unit": "rows",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "ct.csv files",
   "statistic": "rows"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "cut_imi_vm20i_s": {
  "value": 5294.393999999999,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i label rows",
   "statistic": "sum of end minus start"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "cut_imi_vmx30ui_s": {
  "value": 2367.235000000001,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui label rows",
   "statistic": "sum of end minus start"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "cut_minus_doc_abs_s": {
  "value": 0.30500000000029104,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "all label rows",
   "statistic": "absolute difference between labelled cutting time and the article's printed cutting time"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "cut_share_s0": {
  "value": 0.5780706536675358,
  "unit": "fraction",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "all runs",
   "statistic": "labelled cutting time over Sound0 duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "cut_tmf_vf10_s": {
  "value": 9416.676,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10 label rows",
   "statistic": "sum of end minus start"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "cut_total_s": {
  "value": 17078.305,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "all label rows",
   "statistic": "sum of end minus start"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "d10_n_acc0": {
  "value": 41,
  "unit": "paths",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "acc0 RMS",
   "statistic": "matched label 2 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_n_acc1": {
  "value": 41,
  "unit": "paths",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "acc1 RMS",
   "statistic": "matched label 2 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_n_s0": {
  "value": 95,
  "unit": "paths",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "s0 RMS",
   "statistic": "matched label 2 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_n_s1": {
  "value": 41,
  "unit": "paths",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "s1 RMS",
   "statistic": "matched label 2 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_n_s2": {
  "value": 95,
  "unit": "paths",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "s2 RMS",
   "statistic": "matched label 2 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_ratio_acc0": {
  "value": 1.553029518881458,
  "unit": "ratio",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "acc0 RMS",
   "statistic": "median log ratio of RMS, defective over matched good (reported as a ratio)"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_ratio_acc0_ci": {
  "value": null,
  "lo": 0.999936364871723,
  "hi": 1.6453561656236861,
  "level": 0.95,
  "method": "run bootstrap of the median log ratio, exponentiated; 4000 of 4000 resamples usable",
  "point": "d10_ratio_acc0",
  "unit": "ratio",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "acc0 RMS",
   "statistic": "median log ratio of RMS, defective over matched good (reported as a ratio)"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d10_ratio_acc1": {
  "value": 1.0565943548567094,
  "unit": "ratio",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "acc1 RMS",
   "statistic": "median log ratio of RMS, defective over matched good (reported as a ratio)"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_ratio_acc1_ci": {
  "value": null,
  "lo": 1.0142015405336646,
  "hi": 1.1638738475070793,
  "level": 0.95,
  "method": "run bootstrap of the median log ratio, exponentiated; 4000 of 4000 resamples usable",
  "point": "d10_ratio_acc1",
  "unit": "ratio",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "acc1 RMS",
   "statistic": "median log ratio of RMS, defective over matched good (reported as a ratio)"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d10_ratio_s0": {
  "value": 1.3904411962089798,
  "unit": "ratio",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "s0 RMS",
   "statistic": "median log ratio of RMS, defective over matched good (reported as a ratio)"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_ratio_s0_ci": {
  "value": null,
  "lo": 1.0198626243991542,
  "hi": 1.9032775437314033,
  "level": 0.95,
  "method": "run bootstrap of the median log ratio, exponentiated; 4000 of 4000 resamples usable",
  "point": "d10_ratio_s0",
  "unit": "ratio",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "s0 RMS",
   "statistic": "median log ratio of RMS, defective over matched good (reported as a ratio)"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d10_ratio_s1": {
  "value": 1.0129063580835453,
  "unit": "ratio",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "s1 RMS",
   "statistic": "median log ratio of RMS, defective over matched good (reported as a ratio)"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_ratio_s1_ci": {
  "value": null,
  "lo": 0.9693258678816514,
  "hi": 1.0974298573362398,
  "level": 0.95,
  "method": "run bootstrap of the median log ratio, exponentiated; 4000 of 4000 resamples usable",
  "point": "d10_ratio_s1",
  "unit": "ratio",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "s1 RMS",
   "statistic": "median log ratio of RMS, defective over matched good (reported as a ratio)"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d10_ratio_s2": {
  "value": 1.3394685814972038,
  "unit": "ratio",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "s2 RMS",
   "statistic": "median log ratio of RMS, defective over matched good (reported as a ratio)"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_ratio_s2_ci": {
  "value": null,
  "lo": 0.9321644725073756,
  "hi": 1.6206293607649016,
  "level": 0.95,
  "method": "run bootstrap of the median log ratio, exponentiated; 4000 of 4000 resamples usable",
  "point": "d10_ratio_s2",
  "unit": "ratio",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "s2 RMS",
   "statistic": "median log ratio of RMS, defective over matched good (reported as a ratio)"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d10_runs_acc0": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "acc0 RMS",
   "statistic": "runs with matched label 2 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_runs_acc1": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "acc1 RMS",
   "statistic": "runs with matched label 2 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_runs_s0": {
  "value": 4,
  "unit": "runs",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "s0 RMS",
   "statistic": "runs with matched label 2 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_runs_s1": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "s1 RMS",
   "statistic": "runs with matched label 2 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d10_runs_s2": {
  "value": 4,
  "unit": "runs",
  "estimand": {
   "population": "label 2 paths with a label 0 path of the good counterpart tool on the same machine, workpiece, ap, ae, N, F and direction",
   "filter": "s2 RMS",
   "statistic": "runs with matched label 2 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d1_l1_paths_aa6061": {
  "value": 362,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, AA 6061",
   "statistic": "label 1 paths"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_paths_aa6061_other": {
  "value": 1,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, AA 6061, tools other than A and A*",
   "statistic": "label 1 paths"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_paths_aa6061_toola": {
  "value": 361,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, AA 6061, tool A or A*",
   "statistic": "label 1 paths"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_share_tool_a": {
  "value": 0.650853889943074,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool A",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_share_tool_astar": {
  "value": 1.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool A*",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_share_tool_b": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool B",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_share_tool_bstar": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool B*",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_share_tool_c1": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool C1",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_share_tool_c2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool C2",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_share_tool_d": {
  "value": 0.0064794816414686825,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool D",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_share_tool_dstar": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool D*",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_share_tool_e": {
  "value": 0.06727037516170763,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool E",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_share_tool_estar": {
  "value": 0.23026315789473684,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool E*",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_share_tool_f1": {
  "value": 0.015384615384615385,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool F1",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_share_tool_f2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool F2",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_time_share_tool_a": {
  "value": 0.6346730574418046,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool A",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_time_share_tool_astar": {
  "value": 1.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool A*",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_time_share_tool_b": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool B",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_time_share_tool_bstar": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool B*",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_time_share_tool_c1": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool C1",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_time_share_tool_c2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool C2",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_time_share_tool_d": {
  "value": 0.008210080878013991,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool D",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_time_share_tool_dstar": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool D*",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_time_share_tool_e": {
  "value": 0.04542485363079328,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool E",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_time_share_tool_estar": {
  "value": 0.19729485565428137,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool E*",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_time_share_tool_f1": {
  "value": 0.00976906589667432,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool F1",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l1_time_share_tool_f2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool F2",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l2_share_tool_a": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool A",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l2_share_tool_astar": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool A*",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l2_share_tool_b": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool B",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l2_share_tool_bstar": {
  "value": 1.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool B*",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l2_share_tool_c1": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool C1",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l2_share_tool_c2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool C2",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l2_share_tool_d": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool D",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l2_share_tool_dstar": {
  "value": 1.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool D*",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l2_share_tool_e": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool E",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l2_share_tool_estar": {
  "value": 0.7697368421052632,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool E*",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l2_share_tool_f1": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool F1",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_l2_share_tool_f2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool F2",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_n_lab_l1": {
  "value": 458,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, label 1",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_aa6061_other": {
  "value": 293,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, AA 6061, tools other than A and A*",
   "statistic": "paths"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_aa6061_toola": {
  "value": 545,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, AA 6061, tool A or A*",
   "statistic": "paths"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_tool_a": {
  "value": 527,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool A",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_tool_astar": {
  "value": 24,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool A*",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_tool_b": {
  "value": 128,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool B",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_tool_bstar": {
  "value": 36,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool B*",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_tool_c1": {
  "value": 128,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool C1",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_tool_c2": {
  "value": 128,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool C2",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_tool_d": {
  "value": 463,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool D",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_tool_dstar": {
  "value": 24,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool D*",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_tool_e": {
  "value": 773,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool E",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_tool_estar": {
  "value": 152,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool E*",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_tool_f1": {
  "value": 65,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool F1",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_paths_tool_f2": {
  "value": 64,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool F2",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_runs_tool_a": {
  "value": 12,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool A",
   "statistic": "runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_runs_tool_astar": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool A*",
   "statistic": "runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_runs_tool_b": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool B",
   "statistic": "runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_runs_tool_bstar": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool B*",
   "statistic": "runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_runs_tool_c1": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool C1",
   "statistic": "runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_runs_tool_c2": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool C2",
   "statistic": "runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_runs_tool_d": {
  "value": 9,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool D",
   "statistic": "runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_runs_tool_dstar": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool D*",
   "statistic": "runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_runs_tool_e": {
  "value": 16,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool E",
   "statistic": "runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_runs_tool_estar": {
  "value": 4,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool E*",
   "statistic": "runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_runs_tool_f1": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool F1",
   "statistic": "runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_runs_tool_f2": {
  "value": 1,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool F2",
   "statistic": "runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_toola_paths_on_aa6061_share": {
  "value": 0.9891107078039928,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, tool A or A*",
   "statistic": "share of paths cut in AA 6061"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_toola_share_of_lab_l1": {
  "value": 0.8013100436681223,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, label 1",
   "statistic": "share of label 1 paths cut with tool A or A*"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d1_toola_share_of_lab_l1_time": {
  "value": 0.9255499327309853,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, label 1",
   "statistic": "share of label 1 cutting time with tool A or A*"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d2_alu_minus_steel": {
  "value": 0.374633236670345,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "laboratory machines, labels 0, 1 and 2",
   "statistic": "share of paths with label 1 on AA 6061 minus the share on the two steels"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_alu_minus_steel_ci": {
  "value": null,
  "lo": 0.17107172888082092,
  "hi": 0.585258644964601,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d2_alu_minus_steel",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "laboratory machines, labels 0, 1 and 2",
   "statistic": "share of paths with label 1 on AA 6061 minus the share on the two steels"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d2_alu_runs": {
  "value": 18,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "AA 6061",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_concord1_imi_vm20i": {
  "value": 83,
  "unit": "pairs",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "imi_vm20i, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "pairs with both labelled 1"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_concord1_imi_vmx30ui": {
  "value": 51,
  "unit": "pairs",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "imi_vmx30ui, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "pairs with both labelled 1"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_disc_down_imi_vm20i": {
  "value": 1,
  "unit": "pairs",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "imi_vm20i, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "pairs with down labelled 1 and up labelled 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_disc_down_imi_vmx30ui": {
  "value": 0,
  "unit": "pairs",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "imi_vmx30ui, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "pairs with down labelled 1 and up labelled 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_disc_up_imi_vm20i": {
  "value": 129,
  "unit": "pairs",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "imi_vm20i, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "pairs with up labelled 1 and down labelled 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_disc_up_imi_vmx30ui": {
  "value": 12,
  "unit": "pairs",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "imi_vmx30ui, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "pairs with up labelled 1 and down labelled 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_or_cc": {
  "value": 0.5,
  "unit": "pairs",
  "estimand": {
   "population": "analysis setting",
   "filter": "D2 odds ratios",
   "statistic": "continuity correction added to each discordant count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_or_updown_imi_vm20i": {
  "value": 86.33333333333333,
  "unit": "odds ratio",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "imi_vm20i, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "odds ratio of label 1 for up against down milling from discordant pairs, 0.5 added to each count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_or_updown_imi_vm20i_ci": {
  "value": null,
  "lo": 27.0,
  "hi": 365.0,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d2_or_updown_imi_vm20i",
  "unit": "odds ratio",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "imi_vm20i, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "odds ratio of label 1 for up against down milling from discordant pairs, 0.5 added to each count"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d2_or_updown_imi_vmx30ui": {
  "value": 25.0,
  "unit": "odds ratio",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "imi_vmx30ui, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "odds ratio of label 1 for up against down milling from discordant pairs, 0.5 added to each count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_or_updown_imi_vmx30ui_ci": {
  "value": null,
  "lo": 1.0,
  "hi": 61.0,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d2_or_updown_imi_vmx30ui",
  "unit": "odds ratio",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "imi_vmx30ui, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "odds ratio of label 1 for up against down milling from discordant pairs, 0.5 added to each count"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d2_pairs_imi_vm20i": {
  "value": 734,
  "unit": "pairs",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "imi_vm20i, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "count of pairs"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_pairs_imi_vmx30ui": {
  "value": 408,
  "unit": "pairs",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "imi_vmx30ui, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "count of pairs"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_steel_runs": {
  "value": 38,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "AISI 1018 or AISI 4140",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_within_auc_ae": {
  "value": 0.625602534086214,
  "unit": "AUC",
  "estimand": {
   "population": "laboratory paths, labels 0 and 1",
   "filter": "pairs of a label 1 and a label 0 path from the same run",
   "statistic": "probability that the label 1 path has the larger ae (ties count half)"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_within_auc_ae_ci": {
  "value": null,
  "lo": 0.5450704225352113,
  "hi": 0.7025431755104058,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d2_within_auc_ae",
  "unit": "AUC",
  "estimand": {
   "population": "laboratory paths, labels 0 and 1",
   "filter": "pairs of a label 1 and a label 0 path from the same run",
   "statistic": "probability that the label 1 path has the larger ae (ties count half)"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d2_within_auc_ap": {
  "value": 0.5,
  "unit": "AUC",
  "estimand": {
   "population": "laboratory paths, labels 0 and 1",
   "filter": "pairs of a label 1 and a label 0 path from the same run",
   "statistic": "probability that the label 1 path has the larger ap (ties count half)"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_within_auc_ap_ci": {
  "value": null,
  "lo": 0.5,
  "hi": 0.5,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d2_within_auc_ap",
  "unit": "AUC",
  "estimand": {
   "population": "laboratory paths, labels 0 and 1",
   "filter": "pairs of a label 1 and a label 0 path from the same run",
   "statistic": "probability that the label 1 path has the larger ap (ties count half)"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d2_within_auc_fz": {
  "value": 0.5539181930863517,
  "unit": "AUC",
  "estimand": {
   "population": "laboratory paths, labels 0 and 1",
   "filter": "pairs of a label 1 and a label 0 path from the same run",
   "statistic": "probability that the label 1 path has the larger fz (ties count half)"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_within_auc_fz_ci": {
  "value": null,
  "lo": 0.4636141333543334,
  "hi": 0.6323071852160881,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d2_within_auc_fz",
  "unit": "AUC",
  "estimand": {
   "population": "laboratory paths, labels 0 and 1",
   "filter": "pairs of a label 1 and a label 0 path from the same run",
   "statistic": "probability that the label 1 path has the larger fz (ties count half)"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d2_within_auc_mrr": {
  "value": 0.6260157003167608,
  "unit": "AUC",
  "estimand": {
   "population": "laboratory paths, labels 0 and 1",
   "filter": "pairs of a label 1 and a label 0 path from the same run",
   "statistic": "probability that the label 1 path has the larger mrr (ties count half)"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_within_auc_mrr_ci": {
  "value": null,
  "lo": 0.5428810503394542,
  "hi": 0.706119595681837,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d2_within_auc_mrr",
  "unit": "AUC",
  "estimand": {
   "population": "laboratory paths, labels 0 and 1",
   "filter": "pairs of a label 1 and a label 0 path from the same run",
   "statistic": "probability that the label 1 path has the larger mrr (ties count half)"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d2_within_auc_n": {
  "value": 0.4517284120644539,
  "unit": "AUC",
  "estimand": {
   "population": "laboratory paths, labels 0 and 1",
   "filter": "pairs of a label 1 and a label 0 path from the same run",
   "statistic": "probability that the label 1 path has the larger N (ties count half)"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_within_auc_n_ci": {
  "value": null,
  "lo": 0.34251094692078415,
  "hi": 0.5721434613765345,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d2_within_auc_n",
  "unit": "AUC",
  "estimand": {
   "population": "laboratory paths, labels 0 and 1",
   "filter": "pairs of a label 1 and a label 0 path from the same run",
   "statistic": "probability that the label 1 path has the larger N (ties count half)"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d2_within_auc_up": {
  "value": 0.797204241839967,
  "unit": "AUC",
  "estimand": {
   "population": "laboratory paths, labels 0 and 1",
   "filter": "pairs of a label 1 and a label 0 path from the same run",
   "statistic": "probability that the label 1 path has the larger up (ties count half)"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d2_within_auc_up_ci": {
  "value": null,
  "lo": 0.7215617650474725,
  "hi": 0.8547122457413104,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d2_within_auc_up",
  "unit": "AUC",
  "estimand": {
   "population": "laboratory paths, labels 0 and 1",
   "filter": "pairs of a label 1 and a label 0 path from the same run",
   "statistic": "probability that the label 1 path has the larger up (ties count half)"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d2_within_runs": {
  "value": 15,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "runs with both label 0 and label 1 paths",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vm20i_acc0": {
  "value": 0.9548429405206899,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, acc0_rms available",
   "statistic": "AUC of acc0_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vm20i_acc0_ci": {
  "value": null,
  "lo": 0.8972443296429086,
  "hi": 0.9932896846348953,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vm20i_acc0",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, acc0_rms available",
   "statistic": "AUC of acc0_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_auc_imi_vm20i_acc1": {
  "value": 0.5917581480752836,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, acc1_rms available",
   "statistic": "AUC of acc1_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vm20i_acc1_ci": {
  "value": null,
  "lo": 0.3190827960552152,
  "hi": 0.96965904522301,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vm20i_acc1",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, acc1_rms available",
   "statistic": "AUC of acc1_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_auc_imi_vm20i_load": {
  "value": 0.6804288805823333,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, mtc_S_Axis_Load available",
   "statistic": "AUC of mtc_S_Axis_Load for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vm20i_load_ci": {
  "value": null,
  "lo": 0.5618337243144306,
  "hi": 0.804082294492312,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vm20i_load",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, mtc_S_Axis_Load available",
   "statistic": "AUC of mtc_S_Axis_Load for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_auc_imi_vm20i_s0": {
  "value": 0.9898957308675979,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, s0_rms available",
   "statistic": "AUC of s0_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vm20i_s0_ci": {
  "value": null,
  "lo": 0.975070740813059,
  "hi": 0.9984519427073114,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vm20i_s0",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, s0_rms available",
   "statistic": "AUC of s0_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_auc_imi_vm20i_s1": {
  "value": 0.5340704308479245,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, s1_rms available",
   "statistic": "AUC of s1_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vm20i_s1_ci": {
  "value": null,
  "lo": 0.4071428332559873,
  "hi": 0.6518670853822678,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vm20i_s1",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, s1_rms available",
   "statistic": "AUC of s1_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_auc_imi_vm20i_s2": {
  "value": 0.8240487900845958,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, s2_rms available",
   "statistic": "AUC of s2_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vm20i_s2_ci": {
  "value": null,
  "lo": 0.7630857764269011,
  "hi": 0.8994692698031544,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vm20i_s2",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, s2_rms available",
   "statistic": "AUC of s2_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_auc_imi_vm20i_smarms": {
  "value": 0.9698629418322513,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, mtc_smarms available",
   "statistic": "AUC of mtc_smarms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vm20i_smarms_ci": {
  "value": null,
  "lo": 0.9394617367194809,
  "hi": 0.99169801670371,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vm20i_smarms",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, mtc_smarms available",
   "statistic": "AUC of mtc_smarms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_auc_imi_vm20i_watt": {
  "value": 0.6120165256738147,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, mtc_watt available",
   "statistic": "AUC of mtc_watt for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vm20i_watt_ci": {
  "value": null,
  "lo": 0.4935947587362026,
  "hi": 0.7406028305913331,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vm20i_watt",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, mtc_watt available",
   "statistic": "AUC of mtc_watt for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_auc_imi_vmx30ui_load": {
  "value": 0.2328157040328093,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, mtc_S_Axis_Load available",
   "statistic": "AUC of mtc_S_Axis_Load for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vmx30ui_load_ci": {
  "value": null,
  "lo": 0.13991679613917804,
  "hi": 0.49481657925407924,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vmx30ui_load",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, mtc_S_Axis_Load available",
   "statistic": "AUC of mtc_S_Axis_Load for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_auc_imi_vmx30ui_s0": {
  "value": 0.8535436602870813,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, s0_rms available",
   "statistic": "AUC of s0_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vmx30ui_s0_ci": {
  "value": null,
  "lo": 0.7131848081926784,
  "hi": 0.991471228976685,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vmx30ui_s0",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, s0_rms available",
   "statistic": "AUC of s0_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_auc_imi_vmx30ui_s2": {
  "value": 0.7881920710868079,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, s2_rms available",
   "statistic": "AUC of s2_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vmx30ui_s2_ci": {
  "value": null,
  "lo": 0.6732595456505389,
  "hi": 0.9204233349550099,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vmx30ui_s2",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, s2_rms available",
   "statistic": "AUC of s2_rms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_auc_imi_vmx30ui_smarms": {
  "value": 0.958032510252905,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, mtc_smarms available",
   "statistic": "AUC of mtc_smarms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vmx30ui_smarms_ci": {
  "value": null,
  "lo": 0.8984245854479967,
  "hi": 0.9874561512363855,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vmx30ui_smarms",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, mtc_smarms available",
   "statistic": "AUC of mtc_smarms for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_auc_imi_vmx30ui_watt": {
  "value": 0.6384248974709501,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, mtc_watt available",
   "statistic": "AUC of mtc_watt for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_auc_imi_vmx30ui_watt_ci": {
  "value": null,
  "lo": 0.41795491217939124,
  "hi": 0.8850422257113834,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d3_auc_imi_vmx30ui_watt",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, mtc_watt available",
   "statistic": "AUC of mtc_watt for label 1 against label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d3_n0_imi_vm20i_acc0": {
  "value": 1173,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, acc0_rms available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n0_imi_vm20i_acc1": {
  "value": 1173,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, acc1_rms available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n0_imi_vm20i_load": {
  "value": 1173,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, mtc_S_Axis_Load available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n0_imi_vm20i_s0": {
  "value": 1173,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, s0_rms available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n0_imi_vm20i_s1": {
  "value": 1173,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, s1_rms available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n0_imi_vm20i_s2": {
  "value": 1173,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, s2_rms available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n0_imi_vm20i_smarms": {
  "value": 1173,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, mtc_smarms available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n0_imi_vm20i_watt": {
  "value": 1173,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, mtc_watt available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n0_imi_vmx30ui_load": {
  "value": 704,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, mtc_S_Axis_Load available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n0_imi_vmx30ui_s0": {
  "value": 704,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, s0_rms available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n0_imi_vmx30ui_s2": {
  "value": 704,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, s2_rms available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n0_imi_vmx30ui_smarms": {
  "value": 704,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, mtc_smarms available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n0_imi_vmx30ui_watt": {
  "value": 704,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, mtc_watt available",
   "statistic": "label 0 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vm20i_acc0": {
  "value": 325,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, acc0_rms available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vm20i_acc1": {
  "value": 325,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, acc1_rms available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vm20i_load": {
  "value": 325,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, mtc_S_Axis_Load available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vm20i_s0": {
  "value": 325,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, s0_rms available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vm20i_s1": {
  "value": 325,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, s1_rms available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vm20i_s2": {
  "value": 325,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, s2_rms available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vm20i_smarms": {
  "value": 325,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, mtc_smarms available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vm20i_watt": {
  "value": 325,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, mtc_watt available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vmx30ui_load": {
  "value": 133,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, mtc_S_Axis_Load available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vmx30ui_s0": {
  "value": 133,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, s0_rms available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vmx30ui_s2": {
  "value": 133,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, s2_rms available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vmx30ui_smarms": {
  "value": 133,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, mtc_smarms available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d3_n1_imi_vmx30ui_watt": {
  "value": 133,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, mtc_watt available",
   "statistic": "label 1 paths"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vm20i_acc0": {
  "value": 0.9653097345132743,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of acc0_rms"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vm20i_acc0_ci": {
  "value": null,
  "lo": 0.9179100142201464,
  "hi": 0.9928034726290673,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d4_auc_imi_vm20i_acc0",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of acc0_rms"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_auc_imi_vm20i_acc1": {
  "value": 0.9497345132743363,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of acc1_rms"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vm20i_acc1_ci": {
  "value": null,
  "lo": 0.9044963086068586,
  "hi": 0.9974753266611065,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d4_auc_imi_vm20i_acc1",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of acc1_rms"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_auc_imi_vm20i_load": {
  "value": 0.5893805309734513,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of mtc_S_Axis_Load"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vm20i_load_ci": {
  "value": null,
  "lo": 0.41957537949578555,
  "hi": 0.7789721930263963,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d4_auc_imi_vm20i_load",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of mtc_S_Axis_Load"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_auc_imi_vm20i_s0": {
  "value": 0.9823008849557522,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of s0_rms"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vm20i_s0_ci": {
  "value": null,
  "lo": 0.9589033683211765,
  "hi": 0.9967159361730338,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d4_auc_imi_vm20i_s0",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of s0_rms"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_auc_imi_vm20i_s1": {
  "value": 0.6516814159292036,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of s1_rms"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vm20i_s1_ci": {
  "value": null,
  "lo": 0.5562483850455019,
  "hi": 0.7829921941899612,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d4_auc_imi_vm20i_s1",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of s1_rms"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_auc_imi_vm20i_s2": {
  "value": 0.9327433628318584,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of s2_rms"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vm20i_s2_ci": {
  "value": null,
  "lo": 0.8763798388100573,
  "hi": 0.9828970206179869,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d4_auc_imi_vm20i_s2",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of s2_rms"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_auc_imi_vm20i_smarms": {
  "value": 0.9426548672566372,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of mtc_smarms"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vm20i_smarms_ci": {
  "value": null,
  "lo": 0.8861264531544364,
  "hi": 0.9892920292280232,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d4_auc_imi_vm20i_smarms",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of mtc_smarms"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_auc_imi_vm20i_watt": {
  "value": 0.52,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of mtc_watt"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vm20i_watt_ci": {
  "value": null,
  "lo": 0.4013826492329925,
  "hi": 0.6635360181381089,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d4_auc_imi_vm20i_watt",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of mtc_watt"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_auc_imi_vmx30ui_load": {
  "value": 0.7100760456273765,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of mtc_S_Axis_Load"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vmx30ui_load_ci": {
  "value": null,
  "lo": 0.0,
  "hi": 0.8048780487804879,
  "level": 0.95,
  "method": "run bootstrap; 3960 of 4000 resamples usable",
  "point": "d4_auc_imi_vmx30ui_load",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of mtc_S_Axis_Load"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_auc_imi_vmx30ui_s0": {
  "value": 0.5532319391634981,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of s0_rms"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vmx30ui_s0_ci": {
  "value": null,
  "lo": 0.32891832229580575,
  "hi": 1.0,
  "level": 0.95,
  "method": "run bootstrap; 3960 of 4000 resamples usable",
  "point": "d4_auc_imi_vmx30ui_s0",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of s0_rms"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_auc_imi_vmx30ui_s2": {
  "value": 0.2965779467680608,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of s2_rms"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vmx30ui_s2_ci": {
  "value": null,
  "lo": 0.14327485380116958,
  "hi": 1.0,
  "level": 0.95,
  "method": "run bootstrap; 3960 of 4000 resamples usable",
  "point": "d4_auc_imi_vmx30ui_s2",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of s2_rms"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_auc_imi_vmx30ui_smarms": {
  "value": 0.4600760456273764,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of mtc_smarms"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vmx30ui_smarms_ci": {
  "value": null,
  "lo": 0.4148471615720524,
  "hi": 1.0,
  "level": 0.95,
  "method": "run bootstrap; 3960 of 4000 resamples usable",
  "point": "d4_auc_imi_vmx30ui_smarms",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of mtc_smarms"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_auc_imi_vmx30ui_watt": {
  "value": 0.2585551330798479,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of mtc_watt"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_auc_imi_vmx30ui_watt_ci": {
  "value": null,
  "lo": 0.0,
  "hi": 0.5204081632653061,
  "level": 0.95,
  "method": "run bootstrap; 3960 of 4000 resamples usable",
  "point": "d4_auc_imi_vmx30ui_watt",
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, strata of run, tool, ap and ae holding both labels",
   "statistic": "within-stratum AUC of mtc_watt"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d4_runs_imi_vm20i": {
  "value": 11,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, same run, tool, ap and ae",
   "statistic": "runs with at least one stratum holding both labels"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_runs_imi_vmx30ui": {
  "value": 4,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, same run, tool, ap and ae",
   "statistic": "runs with at least one stratum holding both labels"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_strata_imi_vm20i": {
  "value": 18,
  "unit": "strata",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, same run, tool, ap and ae",
   "statistic": "strata holding both labels"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d4_strata_imi_vmx30ui": {
  "value": 4,
  "unit": "strata",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, same run, tool, ap and ae",
   "statistic": "strata holding both labels"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_band_hi_hz": {
  "value": 10000.0,
  "unit": "Hz",
  "estimand": {
   "population": "analysis setting",
   "filter": "D5 definition",
   "statistic": "upper edge of the searched band"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_band_lo_hz": {
  "value": 200.0,
  "unit": "Hz",
  "estimand": {
   "population": "analysis setting",
   "filter": "D5 definition",
   "statistic": "lower edge of the searched band"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_comb_bins": {
  "value": 2,
  "unit": "bins",
  "estimand": {
   "population": "analysis setting",
   "filter": "D5 definition",
   "statistic": "bins excluded on each side of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_min_rpm": {
  "value": 3000.0,
  "unit": "rpm",
  "estimand": {
   "population": "analysis setting",
   "filter": "D5 definition",
   "statistic": "lowest spindle speed at which the harmonic comb leaves clean gaps"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_n_imi_vm20i_acc0": {
  "value": 1386,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc0, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_n_imi_vm20i_acc1": {
  "value": 1386,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc1, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_n_imi_vm20i_s1": {
  "value": 1386,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s1, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_n_imi_vm20i_s2": {
  "value": 1386,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s2, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_n_imi_vmx30ui_s2": {
  "value": 725,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, s2, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_diff_imi_vm20i_acc0": {
  "value": 0.6293540201551511,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc0, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "difference in non-harmonic dominant share, label 1 minus label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_diff_imi_vm20i_acc0_ci": {
  "value": null,
  "lo": 0.5087835335640514,
  "hi": 0.7361775212212357,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_diff_imi_vm20i_acc0",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc0, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "difference in non-harmonic dominant share, label 1 minus label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_diff_imi_vm20i_acc1": {
  "value": 0.5901283259624448,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc1, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "difference in non-harmonic dominant share, label 1 minus label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_diff_imi_vm20i_acc1_ci": {
  "value": null,
  "lo": 0.47677268847374304,
  "hi": 0.6940578525187526,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_diff_imi_vm20i_acc1",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc1, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "difference in non-harmonic dominant share, label 1 minus label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_diff_imi_vm20i_s1": {
  "value": 0.20457333430000726,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s1, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "difference in non-harmonic dominant share, label 1 minus label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_diff_imi_vm20i_s1_ci": {
  "value": null,
  "lo": 0.14833166420612587,
  "hi": 0.307109103570425,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_diff_imi_vm20i_s1",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s1, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "difference in non-harmonic dominant share, label 1 minus label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_diff_imi_vm20i_s2": {
  "value": 0.5622997172478793,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s2, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "difference in non-harmonic dominant share, label 1 minus label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_diff_imi_vm20i_s2_ci": {
  "value": null,
  "lo": 0.44198802766745315,
  "hi": 0.6590404235395515,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_diff_imi_vm20i_s2",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s2, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "difference in non-harmonic dominant share, label 1 minus label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_diff_imi_vmx30ui_s2": {
  "value": 0.3270930705141232,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, s2, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "difference in non-harmonic dominant share, label 1 minus label 0"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_diff_imi_vmx30ui_s2_ci": {
  "value": null,
  "lo": 0.13840190119993795,
  "hi": 0.45126779756972085,
  "level": 0.95,
  "method": "run bootstrap; 3999 of 4000 resamples usable",
  "point": "d5_nhdom_diff_imi_vmx30ui_s2",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, s2, spindle speed at least 3000 rpm, labels 0 and 1",
   "statistic": "difference in non-harmonic dominant share, label 1 minus label 0"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_imi_vm20i_acc0_l0": {
  "value": 0.05372290292177191,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc0, spindle speed at least 3000 rpm, label 0",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_imi_vm20i_acc0_l0_ci": {
  "value": null,
  "lo": 0.02165023394275759,
  "hi": 0.09852261695012765,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_imi_vm20i_acc0_l0",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc0, spindle speed at least 3000 rpm, label 0",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_imi_vm20i_acc0_l1": {
  "value": 0.683076923076923,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc0, spindle speed at least 3000 rpm, label 1",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_imi_vm20i_acc0_l1_ci": {
  "value": null,
  "lo": 0.5593174776745033,
  "hi": 0.7816948671897155,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_imi_vm20i_acc0_l1",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc0, spindle speed at least 3000 rpm, label 1",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_imi_vm20i_acc1_l0": {
  "value": 0.12064090480678605,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc1, spindle speed at least 3000 rpm, label 0",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_imi_vm20i_acc1_l0_ci": {
  "value": null,
  "lo": 0.08977679842797746,
  "hi": 0.15393519800753966,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_imi_vm20i_acc1_l0",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc1, spindle speed at least 3000 rpm, label 0",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_imi_vm20i_acc1_l1": {
  "value": 0.7107692307692308,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc1, spindle speed at least 3000 rpm, label 1",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_imi_vm20i_acc1_l1_ci": {
  "value": null,
  "lo": 0.5939004858980972,
  "hi": 0.8083338452088452,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_imi_vm20i_acc1_l1",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, acc1, spindle speed at least 3000 rpm, label 1",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_imi_vm20i_s1_l0": {
  "value": 0.016965127238454288,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s1, spindle speed at least 3000 rpm, label 0",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_imi_vm20i_s1_l0_ci": {
  "value": null,
  "lo": 0.001092896174863388,
  "hi": 0.04064291422687384,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_imi_vm20i_s1_l0",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s1, spindle speed at least 3000 rpm, label 0",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_imi_vm20i_s1_l1": {
  "value": 0.22153846153846155,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s1, spindle speed at least 3000 rpm, label 1",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_imi_vm20i_s1_l1_ci": {
  "value": null,
  "lo": 0.1649952672605791,
  "hi": 0.3252160278745645,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_imi_vm20i_s1_l1",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s1, spindle speed at least 3000 rpm, label 1",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_imi_vm20i_s2_l0": {
  "value": 0.03770028275212064,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s2, spindle speed at least 3000 rpm, label 0",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_imi_vm20i_s2_l0_ci": {
  "value": null,
  "lo": 0.015538032276076962,
  "hi": 0.06999545965779189,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_imi_vm20i_s2_l0",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s2, spindle speed at least 3000 rpm, label 0",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_imi_vm20i_s2_l1": {
  "value": 0.6,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s2, spindle speed at least 3000 rpm, label 1",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_imi_vm20i_s2_l1_ci": {
  "value": null,
  "lo": 0.47952887837571456,
  "hi": 0.6897429417098311,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_imi_vm20i_s2_l1",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, s2, spindle speed at least 3000 rpm, label 1",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_imi_vmx30ui_s2_l0": {
  "value": 0.2668918918918919,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, s2, spindle speed at least 3000 rpm, label 0",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_imi_vmx30ui_s2_l0_ci": {
  "value": null,
  "lo": 0.1312721613742022,
  "hi": 0.419223134943659,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5_nhdom_imi_vmx30ui_s2_l0",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, s2, spindle speed at least 3000 rpm, label 0",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5_nhdom_imi_vmx30ui_s2_l1": {
  "value": 0.5939849624060151,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, s2, spindle speed at least 3000 rpm, label 1",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5_nhdom_imi_vmx30ui_s2_l1_ci": {
  "value": null,
  "lo": 0.4375,
  "hi": 0.6454545454545455,
  "level": 0.95,
  "method": "run bootstrap; 3999 of 4000 resamples usable",
  "point": "d5_nhdom_imi_vmx30ui_s2_l1",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, s2, spindle speed at least 3000 rpm, label 1",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5x_n_toola_alu_l0": {
  "value": 158,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, tool A on AA 6061, label 0, spindle speed at least 3000 rpm, Acc0",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5x_nhdom_toola_alu_l0": {
  "value": 0.2911392405063291,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, tool A on AA 6061, label 0, spindle speed at least 3000 rpm, Acc0",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d5x_nhdom_toola_alu_l0_ci": {
  "value": null,
  "lo": 0.20967741935483872,
  "hi": 0.45384615384615384,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d5x_nhdom_toola_alu_l0",
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, tool A on AA 6061, label 0, spindle speed at least 3000 rpm, Acc0",
   "statistic": "share of paths whose largest spectral line in 200 Hz to 10 kHz is not within two bins of a spindle harmonic"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d5x_runs_toola_alu_l0": {
  "value": 6,
  "unit": "runs",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, tool A on AA 6061, label 0, spindle speed at least 3000 rpm, Acc0",
   "statistic": "runs"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d6_feed_override_not100_imi_vm20i": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "share of paths whose time-weighted feed override differs from 100 percent"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_feed_override_not100_imi_vmx30ui": {
  "value": 0.06903991370010787,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "share of paths whose time-weighted feed override differs from 100 percent"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_feed_relerr_median_imi_vm20i": {
  "value": -0.007056212071894971,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, paths with an MTConnect feed",
   "statistic": "median of (recorded minus F) over F"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_feed_relerr_median_imi_vmx30ui": {
  "value": -0.10547732470682791,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, paths with an MTConnect feed",
   "statistic": "median of (recorded minus F) over F"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_feed_relerr_median_long_imi_vm20i": {
  "value": -0.0008127657270185452,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, paths above the long-path duration",
   "statistic": "median of (recorded minus F) over F"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_feed_relerr_median_long_imi_vmx30ui": {
  "value": -0.04151808456444128,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, paths above the long-path duration",
   "statistic": "median of (recorded minus F) over F"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_feed_relerr_median_short_imi_vm20i": {
  "value": -0.21791131498471925,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, paths up to the short-path duration",
   "statistic": "median of (recorded minus F) over F"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_feed_relerr_median_short_imi_vmx30ui": {
  "value": -0.7184930816256212,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, paths up to the short-path duration",
   "statistic": "median of (recorded minus F) over F"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_feed_tol": {
  "value": 0.05,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "D6 definition",
   "statistic": "relative tolerance for feed agreement"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_feed_within5pct_imi_vm20i": {
  "value": 0.7022082018927445,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, paths with an MTConnect feed",
   "statistic": "share with recorded feed within 5 percent of F"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_feed_within5pct_imi_vmx30ui": {
  "value": 0.33980582524271846,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, paths with an MTConnect feed",
   "statistic": "share with recorded feed within 5 percent of F"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_feed_within5pct_long_imi_vm20i": {
  "value": 0.7621440536013401,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, paths above the long-path duration with an MTConnect feed",
   "statistic": "share with recorded feed within the feed tolerance of F"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_feed_within5pct_long_imi_vmx30ui": {
  "value": 0.5368171021377672,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, paths above the long-path duration with an MTConnect feed",
   "statistic": "share with recorded feed within the feed tolerance of F"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_long_s": {
  "value": 2.0,
  "unit": "s",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "D6 breakdown",
   "statistic": "lower duration of a long path"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_n_short_imi_vm20i": {
  "value": 15,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, paths up to the short-path duration",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_n_short_imi_vmx30ui": {
  "value": 92,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, paths up to the short-path duration",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_short_s": {
  "value": 1.0,
  "unit": "s",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "D6 breakdown",
   "statistic": "upper duration of a short path"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_speed_n_imi_vm20i": {
  "value": 1585,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "paths with an MTConnect speed"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_speed_n_imi_vmx30ui": {
  "value": 927,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "paths with an MTConnect speed"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_speed_n_tmf_vf10": {
  "value": 63,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10",
   "statistic": "paths with an MTConnect speed"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_speed_relerr_median_imi_vm20i": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, paths with an MTConnect speed",
   "statistic": "median of (recorded minus N) over N"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_speed_relerr_median_imi_vmx30ui": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, paths with an MTConnect speed",
   "statistic": "median of (recorded minus N) over N"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_speed_relerr_median_tmf_vf10": {
  "value": -0.00011011698985845715,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10, paths with an MTConnect speed",
   "statistic": "median of (recorded minus N) over N"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_speed_tol": {
  "value": 0.02,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "D6 definition",
   "statistic": "relative tolerance for spindle speed agreement"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_speed_within2pct_imi_vm20i": {
  "value": 0.9981072555205047,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, paths with an MTConnect speed",
   "statistic": "share with recorded spindle speed within 2 percent of N"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_speed_within2pct_imi_vmx30ui": {
  "value": 0.9956850053937433,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, paths with an MTConnect speed",
   "statistic": "share with recorded spindle speed within 2 percent of N"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_speed_within2pct_tmf_vf10": {
  "value": 1.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10, paths with an MTConnect speed",
   "statistic": "share with recorded spindle speed within 2 percent of N"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_spindle_override_not100_imi_vm20i": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "share of paths whose time-weighted spindle override differs from 100 percent"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d6_spindle_override_not100_imi_vmx30ui": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "share of paths whose time-weighted spindle override differs from 100 percent"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d7_min_dur_s": {
  "value": 3.0,
  "unit": "s",
  "estimand": {
   "population": "analysis setting",
   "filter": "D7 definition",
   "statistic": "shortest path used"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_n_imi_vm20i_aa6061": {
  "value": 174,
  "unit": "paths",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AA 6061, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_n_imi_vm20i_aisi1018": {
  "value": 59,
  "unit": "paths",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AISI 1018, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_n_imi_vm20i_aisi4140": {
  "value": 412,
  "unit": "paths",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AISI 4140, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_n_imi_vmx30ui_aa6061": {
  "value": 27,
  "unit": "paths",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vmx30ui, AA 6061, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_n_imi_vmx30ui_aisi4140": {
  "value": 139,
  "unit": "paths",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vmx30ui, AISI 4140, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_negative_share_imi_vm20i_aa6061": {
  "value": 0.08045977011494253,
  "unit": "fraction",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AA 6061, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "share of paths with power at or below the pre-cut level"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_negative_share_imi_vm20i_aisi1018": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AISI 1018, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "share of paths with power at or below the pre-cut level"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_negative_share_imi_vm20i_aisi4140": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AISI 4140, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "share of paths with power at or below the pre-cut level"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_negative_share_imi_vmx30ui_aa6061": {
  "value": 0.037037037037037035,
  "unit": "fraction",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vmx30ui, AA 6061, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "share of paths with power at or below the pre-cut level"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_negative_share_imi_vmx30ui_aisi4140": {
  "value": 0.014388489208633094,
  "unit": "fraction",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vmx30ui, AISI 4140, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "share of paths with power at or below the pre-cut level"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_paths": {
  "value": 811,
  "unit": "paths",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_pre_from_s": {
  "value": 1.2,
  "unit": "s",
  "estimand": {
   "population": "analysis setting",
   "filter": "D7 definition",
   "statistic": "start of the pre-cut window before the path"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_pre_to_s": {
  "value": 0.2,
  "unit": "s",
  "estimand": {
   "population": "analysis setting",
   "filter": "D7 definition",
   "statistic": "end of the pre-cut window before the path"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_runs_imi_vm20i_aa6061": {
  "value": 8,
  "unit": "runs",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AA 6061, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "runs"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_runs_imi_vm20i_aisi1018": {
  "value": 1,
  "unit": "runs",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AISI 1018, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "runs"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_runs_imi_vm20i_aisi4140": {
  "value": 8,
  "unit": "runs",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AISI 4140, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "runs"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_runs_imi_vmx30ui_aa6061": {
  "value": 3,
  "unit": "runs",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vmx30ui, AA 6061, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "runs"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_runs_imi_vmx30ui_aisi4140": {
  "value": 3,
  "unit": "runs",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vmx30ui, AISI 4140, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "runs"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_speed_tol": {
  "value": 0.02,
  "unit": "fraction",
  "estimand": {
   "population": "analysis setting",
   "filter": "D7 definition",
   "statistic": "tolerance on pre-cut spindle speed against N"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_trim_s": {
  "value": 1.0,
  "unit": "s",
  "estimand": {
   "population": "analysis setting",
   "filter": "D7 definition",
   "statistic": "time trimmed from the start of each path"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_u_imi_vm20i_aa6061": {
  "value": 0.46279299630889903,
  "unit": "J/mm3",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AA 6061, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "least-squares slope through the origin of power above the pre-cut level (W) on programmed MRR (mm3/s), in J/mm3"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_u_imi_vm20i_aa6061_ci": {
  "value": null,
  "lo": -0.3368853656774331,
  "hi": 1.1979564987154752,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d7_u_imi_vm20i_aa6061",
  "unit": "J/mm3",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AA 6061, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "least-squares slope through the origin of power above the pre-cut level (W) on programmed MRR (mm3/s), in J/mm3"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d7_u_imi_vm20i_aisi1018": {
  "value": 4.572749676249911,
  "unit": "J/mm3",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AISI 1018, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "least-squares slope through the origin of power above the pre-cut level (W) on programmed MRR (mm3/s), in J/mm3"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_u_imi_vm20i_aisi1018_ci": {
  "value": null,
  "lo": 4.572749676249911,
  "hi": 4.572749676249911,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d7_u_imi_vm20i_aisi1018",
  "unit": "J/mm3",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AISI 1018, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "least-squares slope through the origin of power above the pre-cut level (W) on programmed MRR (mm3/s), in J/mm3"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d7_u_imi_vm20i_aisi4140": {
  "value": 3.1775351625256,
  "unit": "J/mm3",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AISI 4140, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "least-squares slope through the origin of power above the pre-cut level (W) on programmed MRR (mm3/s), in J/mm3"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_u_imi_vm20i_aisi4140_ci": {
  "value": null,
  "lo": 2.790609704577371,
  "hi": 4.382550437080805,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d7_u_imi_vm20i_aisi4140",
  "unit": "J/mm3",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vm20i, AISI 4140, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "least-squares slope through the origin of power above the pre-cut level (W) on programmed MRR (mm3/s), in J/mm3"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d7_u_imi_vmx30ui_aa6061": {
  "value": 3.661023574983319,
  "unit": "J/mm3",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vmx30ui, AA 6061, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "least-squares slope through the origin of power above the pre-cut level (W) on programmed MRR (mm3/s), in J/mm3"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_u_imi_vmx30ui_aa6061_ci": {
  "value": null,
  "lo": -3.3306274838196472,
  "hi": 11.01565030774679,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d7_u_imi_vmx30ui_aa6061",
  "unit": "J/mm3",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vmx30ui, AA 6061, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "least-squares slope through the origin of power above the pre-cut level (W) on programmed MRR (mm3/s), in J/mm3"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d7_u_imi_vmx30ui_aisi4140": {
  "value": 2.758532142385324,
  "unit": "J/mm3",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vmx30ui, AISI 4140, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "least-squares slope through the origin of power above the pre-cut level (W) on programmed MRR (mm3/s), in J/mm3"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "d7_u_imi_vmx30ui_aisi4140_ci": {
  "value": null,
  "lo": 2.3173976174828312,
  "hi": 3.6248513691419033,
  "level": 0.95,
  "method": "run bootstrap; 4000 of 4000 resamples usable",
  "point": "d7_u_imi_vmx30ui_aisi4140",
  "unit": "J/mm3",
  "estimand": {
   "population": "laboratory label 0 paths",
   "filter": "imi_vmx30ui, AISI 4140, at least 3 s long, pre-cut spindle speed within 2 percent of N, first second trimmed",
   "statistic": "least-squares slope through the origin of power above the pre-cut level (W) on programmed MRR (mm3/s), in J/mm3"
  },
  "source": "p03_stats.py",
  "kind": "interval"
 },
 "d8_corr_median": {
  "value": 0.7440538319543574,
  "unit": "correlation",
  "estimand": {
   "population": "imi_vm20i runs",
   "filter": "Acc0 envelope against each sound channel envelope, 10 Hz",
   "statistic": "median peak correlation of log envelopes"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_lag_abs_max_s": {
  "value": 0.1,
  "unit": "s",
  "estimand": {
   "population": "imi_vm20i runs",
   "filter": "Acc0 envelope against each sound channel envelope, 10 Hz, lags within 5 s",
   "statistic": "max absolute lag at peak correlation"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_lag_over_median_path_imi_vm20i": {
  "value": 0.14929829799940328,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "median in-window load lag over the median path duration"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_corr_median_imi_vm20i": {
  "value": 0.4248144855924356,
  "unit": "correlation",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vm20i, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "median peak correlation"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_corr_median_imi_vmx30ui": {
  "value": 0.04465249157350116,
  "unit": "correlation",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vmx30ui, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "median peak correlation"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_corr_median_inwin_imi_vm20i": {
  "value": 0.5006179343827265,
  "unit": "correlation",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vm20i, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "median peak correlation among runs in the plausible-delay window"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_corr_median_inwin_imi_vmx30ui": {
  "value": 0.08964975575483251,
  "unit": "correlation",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vmx30ui, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "median peak correlation among runs in the plausible-delay window"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_lag_median_inwin_imi_vm20i": {
  "value": 0.5,
  "unit": "s",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vm20i, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "median peak lag among runs in the plausible-delay window (positive: load later)"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_lag_median_inwin_imi_vmx30ui": {
  "value": 0.8,
  "unit": "s",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vmx30ui, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "median peak lag among runs in the plausible-delay window (positive: load later)"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_n_edge_imi_vm20i": {
  "value": 5,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vm20i, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "runs whose peak lag sits at the edge of the search range"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_n_edge_imi_vmx30ui": {
  "value": 6,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vmx30ui, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "runs whose peak lag sits at the edge of the search range"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_n_inwin_imi_vm20i": {
  "value": 21,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vm20i, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "runs whose peak lag lies in the plausible-delay window"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_n_inwin_imi_vmx30ui": {
  "value": 5,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vmx30ui, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "runs whose peak lag lies in the plausible-delay window"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_n_neg_imi_vm20i": {
  "value": 15,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vm20i, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "runs whose peak lag is negative (load earlier)"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_n_neg_imi_vmx30ui": {
  "value": 3,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vmx30ui, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "runs whose peak lag is negative (load earlier)"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_n_runs": {
  "value": 56,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "MTConnect spindle load against Sound2 envelope",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_n_runs_imi_vm20i": {
  "value": 37,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vm20i, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_load_n_runs_imi_vmx30ui": {
  "value": 19,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "imi_vmx30ui, MTConnect spindle load (step-held, 10 Hz) against Sound2 envelope",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_maxlag_s": {
  "value": 5.0,
  "unit": "s",
  "estimand": {
   "population": "D8 definition",
   "filter": "cross-correlation search range",
   "statistic": "largest lag searched in either direction"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_n_pairs": {
  "value": 111,
  "unit": "pairs",
  "estimand": {
   "population": "imi_vm20i runs",
   "filter": "Acc0 envelope against each sound channel envelope, 10 Hz",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_share_lag0": {
  "value": 0.8558558558558559,
  "unit": "fraction",
  "estimand": {
   "population": "imi_vm20i runs",
   "filter": "Acc0 envelope against each sound channel envelope, 10 Hz, lags within 5 s",
   "statistic": "share of pairs with peak correlation at zero lag"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_share_lag_le01": {
  "value": 1.0,
  "unit": "fraction",
  "estimand": {
   "population": "imi_vm20i runs",
   "filter": "Acc0 envelope against each sound channel envelope, 10 Hz, lags within 5 s",
   "statistic": "share of pairs with peak correlation within one 0.1 s step of zero"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_win_hi_s": {
  "value": 1.0,
  "unit": "s",
  "estimand": {
   "population": "D8 description (after seeing the lags)",
   "filter": "load against Sound2",
   "statistic": "upper edge, inclusive, of the window counted as a plausible delay"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d8_win_lo_s": {
  "value": 0.0,
  "unit": "s",
  "estimand": {
   "population": "D8 description (after seeing the lags)",
   "filter": "load against Sound2",
   "statistic": "lower edge, exclusive, of the window counted as a plausible delay"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_fs_share_imi_vm20i_s0": {
  "value": 0.09260554261104607,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, s0",
   "statistic": "share of samples at full scale during cutting"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_fs_share_imi_vm20i_s1": {
  "value": 0.01188887639139311,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, s1",
   "statistic": "share of samples at full scale during cutting"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_fs_share_imi_vm20i_s2": {
  "value": 0.0049921593166910765,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, s2",
   "statistic": "share of samples at full scale during cutting"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_fs_share_imi_vmx30ui_s0": {
  "value": 0.014808494931850873,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, s0",
   "statistic": "share of samples at full scale during cutting"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_fs_share_imi_vmx30ui_s2": {
  "value": 0.025398170228135356,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, s2",
   "statistic": "share of samples at full scale during cutting"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_fs_share_tmf_vf10_s0": {
  "value": 1.0670343406385296e-05,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10, s0",
   "statistic": "share of samples at full scale during cutting"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_fs_share_tmf_vf10_s2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10, s2",
   "statistic": "share of samples at full scale during cutting"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_paths_gt1pct_imi_vm20i_s0": {
  "value": 0.538801261829653,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, s0",
   "statistic": "share of paths with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_paths_gt1pct_imi_vm20i_s0_l0": {
  "value": 0.37681159420289856,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, s0, label 0",
   "statistic": "share of paths with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_paths_gt1pct_imi_vm20i_s0_l1": {
  "value": 1.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, s0, label 1",
   "statistic": "share of paths with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_paths_gt1pct_imi_vm20i_s1": {
  "value": 0.18990536277602524,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, s1",
   "statistic": "share of paths with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_paths_gt1pct_imi_vm20i_s2": {
  "value": 0.06687697160883281,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, s2",
   "statistic": "share of paths with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_paths_gt1pct_imi_vmx30ui_s0": {
  "value": 0.23948220064724918,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, s0",
   "statistic": "share of paths with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_paths_gt1pct_imi_vmx30ui_s2": {
  "value": 0.36138079827400216,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, s2",
   "statistic": "share of paths with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_paths_gt1pct_tmf_vf10_s0": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10, s0",
   "statistic": "share of paths with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_paths_gt1pct_tmf_vf10_s2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10, s2",
   "statistic": "share of paths with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_pre_paths_gt1pct_imi_vm20i_s0": {
  "value": 0.15749525616698293,
  "unit": "fraction",
  "estimand": {
   "population": "pre-cut windows",
   "filter": "imi_vm20i, s0, windows available",
   "statistic": "share of windows with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_pre_paths_gt1pct_imi_vm20i_s1": {
  "value": 0.010120177103099304,
  "unit": "fraction",
  "estimand": {
   "population": "pre-cut windows",
   "filter": "imi_vm20i, s1, windows available",
   "statistic": "share of windows with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_pre_paths_gt1pct_imi_vm20i_s2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "pre-cut windows",
   "filter": "imi_vm20i, s2, windows available",
   "statistic": "share of windows with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_pre_paths_gt1pct_imi_vmx30ui_s0": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "pre-cut windows",
   "filter": "imi_vmx30ui, s0, windows available",
   "statistic": "share of windows with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_pre_paths_gt1pct_imi_vmx30ui_s2": {
  "value": 0.04054054054054054,
  "unit": "fraction",
  "estimand": {
   "population": "pre-cut windows",
   "filter": "imi_vmx30ui, s2, windows available",
   "statistic": "share of windows with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_pre_paths_gt1pct_tmf_vf10_s0": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "pre-cut windows",
   "filter": "tmf_vf10, s0, windows available",
   "statistic": "share of windows with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_pre_paths_gt1pct_tmf_vf10_s2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "pre-cut windows",
   "filter": "tmf_vf10, s2, windows available",
   "statistic": "share of windows with more than 1 percent of samples at full scale"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d9_threshold": {
  "value": 0.01,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "D9 definition",
   "statistic": "share of samples at full scale above which a path counts as clipped"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "d_acc_minus_s0_maxabs": {
  "value": 1.4093385416666706,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "runs with both streams (37)",
   "statistic": "max absolute difference in duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "d_acc_minus_s0_median": {
  "value": -3.906250015006663e-06,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "runs with both streams (37)",
   "statistic": "median difference in duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "d_label_end_minus_s0_maxabs": {
  "value": 358.4203958333334,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "runs with both streams (69)",
   "statistic": "max absolute difference in duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "d_label_end_minus_s0_median": {
  "value": -16.42318750000004,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "runs with both streams (69)",
   "statistic": "median difference in duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "d_mtc_minus_s0_maxabs": {
  "value": 8.999997500000063,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "runs with both streams (69)",
   "statistic": "max absolute difference in duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "d_mtc_minus_s0_median": {
  "value": -0.04208766666670272,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "runs with both streams (69)",
   "statistic": "median difference in duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "d_s1_minus_s0_maxabs": {
  "value": 2.0833333337577642e-05,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "runs with both streams (37)",
   "statistic": "max absolute difference in duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "d_s1_minus_s0_median": {
  "value": 0.0,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "runs with both streams (37)",
   "statistic": "median difference in duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "d_s2_minus_s0_maxabs": {
  "value": 2.0833333337577642e-05,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "runs with both streams (69)",
   "statistic": "max absolute difference in duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "d_s2_minus_s0_median": {
  "value": 0.0,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "runs with both streams (69)",
   "statistic": "median difference in duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "days_since_pub": {
  "value": 151,
  "unit": "days",
  "estimand": {
   "population": "external source dates",
   "filter": "S_article publication date to retrieval date",
   "statistic": "days"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "doc_article_accesses": {
  "value": 3272,
  "unit": "accesses",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_accesses",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_article_citations": {
  "value": 2,
  "unit": "citations",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_citations",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external",
  "note": "citation count displayed on the article page on the retrieval date"
 },
 "doc_article_published": {
  "value": "24 April 2026",
  "unit": "date",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_published",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_audio_bits": {
  "value": 16,
  "unit": "bit",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_sound",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_ct_period_s": {
  "value": 10,
  "unit": "s",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_ct",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_cut_share": {
  "value": 0.58,
  "unit": "fraction",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_durations",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_cut_time_match": {
  "value": 1,
  "unit": "boolean",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "none",
   "statistic": "1 if total labelled cutting time equals the article's cutting time within 1 s"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "doc_cut_time_s": {
  "value": 17078,
  "unit": "s",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_durations",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_duration_imi_vm20i_s": {
  "value": 11788,
  "unit": "s",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_durations",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_duration_imi_vmx30ui_s": {
  "value": 5839,
  "unit": "s",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_durations",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_duration_tmf_vf10_s": {
  "value": 11915,
  "unit": "s",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_durations",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_durations_match_mtc": {
  "value": 0,
  "unit": "boolean",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "none",
   "statistic": "1 if MTConnect span totals equal the article's per-machine durations within 1 s"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "doc_durations_match_s0": {
  "value": 1,
  "unit": "boolean",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "none",
   "statistic": "1 if Sound0 totals equal the article's per-machine durations within 1 s"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "doc_fs_acc_hz": {
  "value": 51200.0,
  "unit": "Hz",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_acc_units",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_fs_audio_hz": {
  "value": 48000,
  "unit": "Hz",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_sound",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_lab_poll_s": {
  "value": 0.1,
  "unit": "s",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_polling",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_lab_sensor_update_s": {
  "value": 1,
  "unit": "s",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_adapter",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_t4_imi_vm20i_l0": {
  "value": 0.6917,
  "unit": "fraction",
  "estimand": {
   "population": "external source",
   "filter": "S_table4",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external",
  "note": "Table 4 cell as printed, divided by 100"
 },
 "doc_t4_imi_vm20i_l1": {
  "value": 0.2633,
  "unit": "fraction",
  "estimand": {
   "population": "external source",
   "filter": "S_table4",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external",
  "note": "Table 4 cell as printed, divided by 100"
 },
 "doc_t4_imi_vm20i_l2": {
  "value": 0.045,
  "unit": "fraction",
  "estimand": {
   "population": "external source",
   "filter": "S_table4",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external",
  "note": "Table 4 cell as printed, divided by 100"
 },
 "doc_t4_imi_vmx30ui_l0": {
  "value": 0.6448,
  "unit": "fraction",
  "estimand": {
   "population": "external source",
   "filter": "S_table4",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external",
  "note": "Table 4 cell as printed, divided by 100"
 },
 "doc_t4_imi_vmx30ui_l1": {
  "value": 0.2771,
  "unit": "fraction",
  "estimand": {
   "population": "external source",
   "filter": "S_table4",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external",
  "note": "Table 4 cell as printed, divided by 100"
 },
 "doc_t4_imi_vmx30ui_l2": {
  "value": 0.078,
  "unit": "fraction",
  "estimand": {
   "population": "external source",
   "filter": "S_table4",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external",
  "note": "Table 4 cell as printed, divided by 100"
 },
 "doc_t4_tmf_vf10_l0": {
  "value": 0.9749,
  "unit": "fraction",
  "estimand": {
   "population": "external source",
   "filter": "S_table4",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external",
  "note": "Table 4 cell as printed, divided by 100"
 },
 "doc_t4_tmf_vf10_l1": {
  "value": 0.025099999999999997,
  "unit": "fraction",
  "estimand": {
   "population": "external source",
   "filter": "S_table4",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external",
  "note": "Table 4 cell as printed, divided by 100"
 },
 "doc_t4_tmf_vf10_l2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "external source",
   "filter": "S_table4",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external",
  "note": "Table 4 cell as printed, divided by 100"
 },
 "doc_total_duration_s": {
  "value": 29543,
  "unit": "s",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_durations",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_vf10_mtc_hz": {
  "value": 1,
  "unit": "Hz",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_vf10_mtc",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "doc_vf10_sound_segment_min": {
  "value": 1,
  "unit": "min",
  "estimand": {
   "population": "external source",
   "filter": "S_article, quote q_vf10_sound",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "dur_max_imi_vm20i": {
  "value": 7.420999999999999,
  "unit": "s",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "max path duration"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "dur_max_imi_vmx30ui": {
  "value": 7.295999999999992,
  "unit": "s",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "max path duration"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "dur_max_tmf_vf10": {
  "value": 408.529,
  "unit": "s",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10",
   "statistic": "max path duration"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "dur_median_imi_vm20i": {
  "value": 3.3489999999999895,
  "unit": "s",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "median path duration"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "dur_median_imi_vmx30ui": {
  "value": 1.7920000000000016,
  "unit": "s",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "median path duration"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "dur_median_tmf_vf10": {
  "value": 132.90599999999998,
  "unit": "s",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10",
   "statistic": "median path duration"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "dur_min_imi_vm20i": {
  "value": 0.5330000000000013,
  "unit": "s",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "min path duration"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "dur_min_imi_vmx30ui": {
  "value": 0.681999999999988,
  "unit": "s",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "min path duration"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "dur_min_tmf_vf10": {
  "value": 1.066000000000031,
  "unit": "s",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10",
   "statistic": "min path duration"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "dur_mtc_imi_vm20i_s": {
  "value": 11794.336075,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i",
   "statistic": "sum over runs of the last MTConnect time stamp"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "dur_mtc_imi_vmx30ui_s": {
  "value": 5837.551256000002,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui",
   "statistic": "sum over runs of the last MTConnect time stamp"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "dur_mtc_tmf_vf10_s": {
  "value": 11906.010719,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10",
   "statistic": "sum over runs of the last MTConnect time stamp"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "dur_s0_imi_vm20i_s": {
  "value": 11788.8215,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i",
   "statistic": "sum over runs of Sound0 WAV duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "dur_s0_imi_vmx30ui_s": {
  "value": 5839.495020833332,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui",
   "statistic": "sum over runs of Sound0 WAV duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "dur_s0_minus_doc_maxabs_s": {
  "value": 0.8215000000000146,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "per machine",
   "statistic": "max absolute difference between the Sound0 total and the article's printed duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "dur_s0_tmf_vf10_s": {
  "value": 11915.312604166667,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10",
   "statistic": "sum over runs of Sound0 WAV duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "dur_s0_total_s": {
  "value": 29543.629125,
  "unit": "s",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "none",
   "statistic": "sum over runs of Sound0 WAV duration"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "env_rate_hz": {
  "value": 10,
  "unit": "Hz",
  "estimand": {
   "population": "10 Hz block-RMS envelopes of acc.csv and WAV files",
   "filter": "d01 and d02 device passes",
   "statistic": "envelope rate"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "env_step_s": {
  "value": 0.1,
  "unit": "s",
  "estimand": {
   "population": "10 Hz block-RMS envelopes of acc.csv and WAV files",
   "filter": "d01 and d02 device passes",
   "statistic": "block length"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "fz_max_imi_vm20i": {
  "value": 0.3951111333333333,
  "unit": "mm per tooth",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "max feed per tooth from F, N, Z"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "fz_max_imi_vmx30ui": {
  "value": 0.31608890666666667,
  "unit": "mm per tooth",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "max feed per tooth from F, N, Z"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "fz_min_imi_vm20i": {
  "value": 0.028448006400000003,
  "unit": "mm per tooth",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "min feed per tooth from F, N, Z"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "fz_min_imi_vmx30ui": {
  "value": 0.028448006400000003,
  "unit": "mm per tooth",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "min feed per tooth from F, N, Z"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "ifm_single_value_share_imi_vm20i": {
  "value": 0.0031545741324921135,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "share of paths in which the spindle-motor vibration RMS takes one value (no update inside the path)"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "ifm_single_value_share_imi_vmx30ui": {
  "value": 0.19741100323624594,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "share of paths in which the spindle-motor vibration RMS takes one value (no update inside the path)"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "j_ijamt_apc_usd": {
  "value": 4090,
  "unit": "USD",
  "estimand": {
   "population": "external source",
   "filter": "The International Journal of Advanced Manufacturing Technology pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_ijamt_first_decision_days": {
  "value": 13,
  "unit": "days",
  "estimand": {
   "population": "external source",
   "filter": "The International Journal of Advanced Manufacturing Technology pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_ijamt_if": {
  "value": 3.7,
  "unit": "impact factor",
  "estimand": {
   "population": "external source",
   "filter": "The International Journal of Advanced Manufacturing Technology pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_ijamt_if5": {
  "value": 3.9,
  "unit": "impact factor",
  "estimand": {
   "population": "external source",
   "filter": "The International Journal of Advanced Manufacturing Technology pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_ijamt_if_year": {
  "value": 2025,
  "unit": "year",
  "estimand": {
   "population": "external source",
   "filter": "The International Journal of Advanced Manufacturing Technology pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jmp_apc_after": {
  "value": "September 14, 2026",
  "unit": "date",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Processes pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jmp_apc_usd": {
  "value": 3200,
  "unit": "USD",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Processes pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jmp_citescore": {
  "value": 11.9,
  "unit": "CiteScore",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Processes pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jmp_gold_from": {
  "value": "January 1, 2027",
  "unit": "date",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Processes pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jmp_if": {
  "value": 7.8,
  "unit": "impact factor",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Processes pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jms_acceptance_days": {
  "value": 138,
  "unit": "days",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Systems pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jms_acceptance_rate": {
  "value": 0.1,
  "unit": "fraction",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Systems pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jms_after_review_days": {
  "value": 53,
  "unit": "days",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Systems pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jms_apc_usd": {
  "value": 4500,
  "unit": "USD",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Systems pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jms_citescore": {
  "value": 27.7,
  "unit": "CiteScore",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Systems pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jms_first_decision_days": {
  "value": 1,
  "unit": "days",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Systems pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jms_if": {
  "value": 14.9,
  "unit": "impact factor",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Systems pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jms_if_year": {
  "value": 2025,
  "unit": "year",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Systems pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_jms_jcr_pub_year": {
  "value": 2026,
  "unit": "year",
  "estimand": {
   "population": "external source",
   "filter": "Journal of Manufacturing Systems pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mfglet_acceptance_days": {
  "value": 125,
  "unit": "days",
  "estimand": {
   "population": "external source",
   "filter": "Manufacturing Letters pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mfglet_after_review_days": {
  "value": 67,
  "unit": "days",
  "estimand": {
   "population": "external source",
   "filter": "Manufacturing Letters pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mfglet_apc_usd": {
  "value": 2000,
  "unit": "USD",
  "estimand": {
   "population": "external source",
   "filter": "Manufacturing Letters pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mfglet_citescore": {
  "value": 4.0,
  "unit": "CiteScore",
  "estimand": {
   "population": "external source",
   "filter": "Manufacturing Letters pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mfglet_decision_weeks": {
  "value": 4,
  "unit": "weeks",
  "estimand": {
   "population": "external source",
   "filter": "Manufacturing Letters pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mfglet_if": {
  "value": 2.8,
  "unit": "impact factor",
  "estimand": {
   "population": "external source",
   "filter": "Manufacturing Letters pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mfglet_if_year": {
  "value": 2025,
  "unit": "year",
  "estimand": {
   "population": "external source",
   "filter": "Manufacturing Letters pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mfglet_jcr_pub_year": {
  "value": 2026,
  "unit": "year",
  "estimand": {
   "population": "external source",
   "filter": "Manufacturing Letters pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mfglet_max_figs": {
  "value": 4,
  "unit": "figures or tables",
  "estimand": {
   "population": "external source",
   "filter": "Manufacturing Letters pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mfglet_word_limit": {
  "value": 1500,
  "unit": "words",
  "estimand": {
   "population": "external source",
   "filter": "Manufacturing Letters pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mssp_acceptance_days": {
  "value": 138,
  "unit": "days",
  "estimand": {
   "population": "external source",
   "filter": "Mechanical Systems and Signal Processing pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mssp_acceptance_rate": {
  "value": 0.2,
  "unit": "fraction",
  "estimand": {
   "population": "external source",
   "filter": "Mechanical Systems and Signal Processing pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mssp_after_review_days": {
  "value": 62,
  "unit": "days",
  "estimand": {
   "population": "external source",
   "filter": "Mechanical Systems and Signal Processing pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mssp_apc_usd": {
  "value": 5020,
  "unit": "USD",
  "estimand": {
   "population": "external source",
   "filter": "Mechanical Systems and Signal Processing pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mssp_citescore": {
  "value": 17.6,
  "unit": "CiteScore",
  "estimand": {
   "population": "external source",
   "filter": "Mechanical Systems and Signal Processing pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mssp_first_decision_days": {
  "value": 11,
  "unit": "days",
  "estimand": {
   "population": "external source",
   "filter": "Mechanical Systems and Signal Processing pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mssp_if": {
  "value": 10.2,
  "unit": "impact factor",
  "estimand": {
   "population": "external source",
   "filter": "Mechanical Systems and Signal Processing pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mssp_if_year": {
  "value": 2025,
  "unit": "year",
  "estimand": {
   "population": "external source",
   "filter": "Mechanical Systems and Signal Processing pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "j_mssp_jcr_pub_year": {
  "value": 2026,
  "unit": "year",
  "estimand": {
   "population": "external source",
   "filter": "Mechanical Systems and Signal Processing pages, retrieved 2026-09-22",
   "statistic": "value as printed"
  },
  "source": "p05_ideas.py",
  "kind": "external"
 },
 "kaggle_downloads": {
  "value": 8569,
  "unit": "downloads",
  "estimand": {
   "population": "external source",
   "filter": "S_kaggle_api",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "kaggle_last_updated": {
  "value": "2026-03-13",
  "unit": "date",
  "estimand": {
   "population": "external source",
   "filter": "S_kaggle_api",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "kaggle_notebooks": {
  "value": 1,
  "unit": "notebooks",
  "estimand": {
   "population": "external source",
   "filter": "S_kaggle_api",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "kaggle_topics": {
  "value": 0,
  "unit": "discussion topics",
  "estimand": {
   "population": "external source",
   "filter": "S_kaggle_api",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "kaggle_total_bytes": {
  "value": 40412136430,
  "unit": "bytes",
  "estimand": {
   "population": "external source",
   "filter": "S_kaggle_api",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "kaggle_version": {
  "value": 2,
  "unit": "version",
  "estimand": {
   "population": "external source",
   "filter": "S_kaggle_api",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "kaggle_views": {
  "value": 2139,
  "unit": "views",
  "estimand": {
   "population": "external source",
   "filter": "S_kaggle_api",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "kaggle_votes": {
  "value": 4,
  "unit": "votes",
  "estimand": {
   "population": "external source",
   "filter": "S_kaggle_api",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "l1_time_cover_share": {
  "value": 0.5,
  "unit": "fraction",
  "estimand": {
   "population": "runs",
   "filter": "definition",
   "statistic": "share of label 1 cutting time used for n_runs_half_l1_time_*"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "l1_time_share_top_runs_imi_vm20i": {
  "value": 0.5597213753326782,
  "unit": "fraction",
  "estimand": {
   "population": "runs",
   "filter": "imi_vm20i",
   "statistic": "share of all label 1 cutting time held by those fewest runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "l1_time_share_top_runs_imi_vmx30ui": {
  "value": 0.8755998585339977,
  "unit": "fraction",
  "estimand": {
   "population": "runs",
   "filter": "imi_vmx30ui",
   "statistic": "share of all label 1 cutting time held by those fewest runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "l1_time_share_top_runs_tmf_vf10": {
  "value": 0.8992412785048622,
  "unit": "fraction",
  "estimand": {
   "population": "runs",
   "filter": "tmf_vf10",
   "statistic": "share of all label 1 cutting time held by those fewest runs"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "lab_l1_path_share_aa6061": {
  "value": 0.431980906921241,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, workpiece AA 6061",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "lab_l1_path_share_aisi1018": {
  "value": 0.06431273644388398,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, workpiece AISI 1018",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "lab_l1_path_share_aisi4140": {
  "value": 0.0510783200908059,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, workpiece AISI 4140",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "lab_n_paths_aa6061": {
  "value": 838,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, workpiece AA 6061",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "lab_n_paths_aisi1018": {
  "value": 793,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, workpiece AISI 1018",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "lab_n_paths_aisi4140": {
  "value": 881,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "laboratory machines, workpiece AISI 4140",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "lab_tools_per_run_max": {
  "value": 1,
  "unit": "tools",
  "estimand": {
   "population": "laboratory runs",
   "filter": "label.csv",
   "statistic": "most distinct tools in one run"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "label_cols_max": {
  "value": 12,
  "unit": "columns",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "label.csv files (69)",
   "statistic": "most columns"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "label_cols_min": {
  "value": 12,
  "unit": "columns",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "label.csv files (69)",
   "statistic": "fewest columns"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "max_run_l1_path_share_imi_vm20i": {
  "value": 1.0,
  "unit": "fraction",
  "estimand": {
   "population": "runs",
   "filter": "imi_vm20i",
   "statistic": "largest within-run share of label 1 paths"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "max_run_l1_path_share_imi_vmx30ui": {
  "value": 1.0,
  "unit": "fraction",
  "estimand": {
   "population": "runs",
   "filter": "imi_vmx30ui",
   "statistic": "largest within-run share of label 1 paths"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "max_run_l1_path_share_tmf_vf10": {
  "value": 0.3333333333333333,
  "unit": "fraction",
  "estimand": {
   "population": "runs",
   "filter": "tmf_vf10",
   "statistic": "largest within-run share of label 1 paths"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "min_runs_interpretable": {
  "value": 5,
  "unit": "runs",
  "estimand": {
   "population": "analyst judgment",
   "filter": "fewest runs for which a run-bootstrap interval is read as an interval",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "months_since_pub": {
  "value": 5,
  "unit": "months",
  "estimand": {
   "population": "external source dates",
   "filter": "S_article publication date to retrieval date",
   "statistic": "months, rounded up"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "mtc_cols_imi_vm20i_max": {
  "value": 52,
  "unit": "columns",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i mtc.csv files (37)",
   "statistic": "most columns"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_cols_imi_vm20i_min": {
  "value": 52,
  "unit": "columns",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i mtc.csv files (37)",
   "statistic": "fewest columns"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_cols_imi_vmx30ui_max": {
  "value": 56,
  "unit": "columns",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui mtc.csv files (19)",
   "statistic": "most columns"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_cols_imi_vmx30ui_min": {
  "value": 56,
  "unit": "columns",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui mtc.csv files (19)",
   "statistic": "fewest columns"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_cols_tmf_vf10_max": {
  "value": 21,
  "unit": "columns",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10 mtc.csv files (13)",
   "statistic": "most columns"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_cols_tmf_vf10_min": {
  "value": 21,
  "unit": "columns",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10 mtc.csv files (13)",
   "statistic": "fewest columns"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_dt_max_imi_vm20i": {
  "value": 3.1769880000000015,
  "unit": "s",
  "estimand": {
   "population": "imi_vm20i mtc.csv rows",
   "filter": "consecutive rows within a run",
   "statistic": "max time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_dt_max_imi_vmx30ui": {
  "value": 4.126289,
  "unit": "s",
  "estimand": {
   "population": "imi_vmx30ui mtc.csv rows",
   "filter": "consecutive rows within a run",
   "statistic": "max time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_dt_max_tmf_vf10": {
  "value": 48.60351300000002,
  "unit": "s",
  "estimand": {
   "population": "tmf_vf10 mtc.csv rows",
   "filter": "consecutive rows within a run",
   "statistic": "max time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_dt_median_imi_vm20i": {
  "value": 0.1450060000000093,
  "unit": "s",
  "estimand": {
   "population": "imi_vm20i mtc.csv rows",
   "filter": "consecutive rows within a run",
   "statistic": "median time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_dt_median_imi_vmx30ui": {
  "value": 0.14648549999999716,
  "unit": "s",
  "estimand": {
   "population": "imi_vmx30ui mtc.csv rows",
   "filter": "consecutive rows within a run",
   "statistic": "median time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_dt_median_tmf_vf10": {
  "value": 0.06523199999999463,
  "unit": "s",
  "estimand": {
   "population": "tmf_vf10 mtc.csv rows",
   "filter": "consecutive rows within a run",
   "statistic": "median time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_dt_p99_imi_vm20i": {
  "value": 0.2136441699999774,
  "unit": "s",
  "estimand": {
   "population": "imi_vm20i mtc.csv rows",
   "filter": "consecutive rows within a run",
   "statistic": "99th percentile time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_dt_p99_imi_vmx30ui": {
  "value": 0.25124835999999806,
  "unit": "s",
  "estimand": {
   "population": "imi_vmx30ui mtc.csv rows",
   "filter": "consecutive rows within a run",
   "statistic": "99th percentile time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_dt_p99_tmf_vf10": {
  "value": 3.0104830999999823,
  "unit": "s",
  "estimand": {
   "population": "tmf_vf10 mtc.csv rows",
   "filter": "consecutive rows within a run",
   "statistic": "99th percentile time step"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_gap_thr_s": {
  "value": 1.0,
  "unit": "s",
  "estimand": {
   "population": "mtc.csv rows",
   "filter": "profile definition",
   "statistic": "time step above which a step counts as a gap"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "mtc_rows_imi_vm20i": {
  "value": 78567,
  "unit": "rows",
  "estimand": {
   "population": "imi_vm20i mtc.csv rows",
   "filter": "all",
   "statistic": "rows"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_rows_imi_vmx30ui": {
  "value": 37779,
  "unit": "rows",
  "estimand": {
   "population": "imi_vmx30ui mtc.csv rows",
   "filter": "all",
   "statistic": "rows"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_rows_tmf_vf10": {
  "value": 26929,
  "unit": "rows",
  "estimand": {
   "population": "tmf_vf10 mtc.csv rows",
   "filter": "all",
   "statistic": "rows"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_share_dt_gt1_imi_vm20i": {
  "value": 0.00014007385712466573,
  "unit": "fraction",
  "estimand": {
   "population": "imi_vm20i mtc.csv rows",
   "filter": "consecutive rows within a run",
   "statistic": "share of steps over 1 s"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_share_dt_gt1_imi_vmx30ui": {
  "value": 0.000847457627118644,
  "unit": "fraction",
  "estimand": {
   "population": "imi_vmx30ui mtc.csv rows",
   "filter": "consecutive rows within a run",
   "statistic": "share of steps over 1 s"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "mtc_share_dt_gt1_tmf_vf10": {
  "value": 0.2681304800118888,
  "unit": "fraction",
  "estimand": {
   "population": "tmf_vf10 mtc.csv rows",
   "filter": "consecutive rows within a run",
   "statistic": "share of steps over 1 s"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_availability_checks": {
  "value": 27,
  "unit": "checks",
  "estimand": {
   "population": "machine by modality cells",
   "filter": "documented availability",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_availability_mismatch": {
  "value": 0,
  "unit": "checks",
  "estimand": {
   "population": "machine by modality cells",
   "filter": "documented availability",
   "statistic": "cells where files disagree with the article"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_closest_s1": {
  "value": 9,
  "unit": "works",
  "estimand": {
   "population": "novelty.json",
   "filter": "S1",
   "statistic": "closest works listed"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "n_closest_s2": {
  "value": 7,
  "unit": "works",
  "estimand": {
   "population": "novelty.json",
   "filter": "S2",
   "statistic": "closest works listed"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "n_criteria": {
  "value": 6,
  "unit": "criteria",
  "estimand": {
   "population": "rubric",
   "filter": "none",
   "statistic": "count"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "n_csv_bom": {
  "value": 56,
  "unit": "files",
  "estimand": {
   "population": "CSV files other than acc.csv",
   "filter": "all",
   "statistic": "files starting with a UTF-8 byte order mark"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_csv_crlf": {
  "value": 151,
  "unit": "files",
  "estimand": {
   "population": "CSV files other than acc.csv",
   "filter": "all",
   "statistic": "files with CRLF line endings"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_csv_small": {
  "value": 151,
  "unit": "files",
  "estimand": {
   "population": "CSV files other than acc.csv",
   "filter": "all",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_dataset_users": {
  "value": 3,
  "unit": "users",
  "estimand": {
   "population": "retrieved works and repositories using the dataset",
   "filter": "papers and code repositories, Kaggle notebooks excluded",
   "statistic": "count"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "n_dup_nc_files": {
  "value": 6,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "NC programs",
   "statistic": "files whose sha256 equals another NC file"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_dup_small_files_non_nc": {
  "value": 0,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "files under 50 MB other than NC programs",
   "statistic": "files whose sha256 equals another file's"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files": {
  "value": 481,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "none",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files_acc": {
  "value": 37,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "file kind acc",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files_ct": {
  "value": 13,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "file kind ct",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files_label": {
  "value": 69,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "file kind label",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files_mtc": {
  "value": 69,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "file kind mtc",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files_nc": {
  "value": 56,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "file kind nc",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files_png": {
  "value": 2,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "file kind png",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files_s0": {
  "value": 69,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "file kind s0",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files_s1": {
  "value": 37,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "file kind s1",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files_s2": {
  "value": 69,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "file kind s2",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files_video": {
  "value": 56,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "file kind video",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files_xlsx": {
  "value": 1,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "file kind xlsx",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_files_xml": {
  "value": 3,
  "unit": "files",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "file kind xml",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_ideas": {
  "value": 16,
  "unit": "ideas",
  "estimand": {
   "population": "inputs/ideas.json",
   "filter": "none",
   "statistic": "count"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "n_journals_verified": {
  "value": 5,
  "unit": "journals",
  "estimand": {
   "population": "inputs/journals.json",
   "filter": "tag VERIFIED or PARTIAL",
   "statistic": "count"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "n_lab_runs_override_not100": {
  "value": 1,
  "unit": "runs",
  "estimand": {
   "population": "laboratory runs",
   "filter": "median time-weighted feed override over the run's paths",
   "statistic": "runs whose median differs from 100 percent"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_label_end_beyond_s0": {
  "value": 0,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "none",
   "statistic": "runs whose last label end is after the end of Sound0"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_machines": {
  "value": 3,
  "unit": "machines",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "none",
   "statistic": "count of machine folders"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_materials": {
  "value": 4,
  "unit": "materials",
  "estimand": {
   "population": "dataset_summary.xlsx",
   "filter": "none",
   "statistic": "distinct workpiece materials"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_max_imi_vm20i": {
  "value": 12000.0,
  "unit": "rpm",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "max spindle speed"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_max_imi_vmx30ui": {
  "value": 12000.0,
  "unit": "rpm",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "max spindle speed"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_min_imi_vm20i": {
  "value": 1500.0,
  "unit": "rpm",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "min spindle speed"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_min_imi_vmx30ui": {
  "value": 1500.0,
  "unit": "rpm",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "min spindle speed"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_mtc_bom": {
  "value": 56,
  "unit": "files",
  "estimand": {
   "population": "mtc.csv files",
   "filter": "all",
   "statistic": "files starting with a UTF-8 byte order mark"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_mtc_unavailable_other": {
  "value": 0,
  "unit": "cells",
  "estimand": {
   "population": "mtc.csv files",
   "filter": "all columns except the asset-change and asset-removal events",
   "statistic": "cells that are UNAVAILABLE or empty"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_near": {
  "value": 2,
  "unit": "ideas",
  "estimand": {
   "population": "idea scores",
   "filter": "weighted score within the near band below the threshold",
   "statistic": "count"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "n_nonpositive_dur": {
  "value": 0,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "all",
   "statistic": "paths with end not after start"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_packages": {
  "value": 2,
  "unit": "papers",
  "estimand": {
   "population": "packages",
   "filter": "none",
   "statistic": "count"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "n_path_duplicates": {
  "value": 0,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "within a run",
   "statistic": "rows repeating an earlier start and end"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_path_overlaps": {
  "value": 0,
  "unit": "pairs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "consecutive paths within a run",
   "statistic": "pairs where a path starts before the previous one ends"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths": {
  "value": 2575,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "all machines",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_ae_gt_d": {
  "value": 0,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "all",
   "statistic": "paths with radial width of cut above the tool diameter"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_d_mismatch_table2": {
  "value": 128,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "all",
   "statistic": "paths whose tool diameter differs from the article's Table 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_defective": {
  "value": 236,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "asterisk tools",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_defective_l1": {
  "value": 59,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "asterisk tools labelled 1",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_imi_vm20i": {
  "value": 1585,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_imi_vm20i_l0": {
  "value": 1173,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, label 0",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_imi_vm20i_l1": {
  "value": 325,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, label 1",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_imi_vm20i_l2": {
  "value": 87,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i, label 2",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_imi_vmx30ui": {
  "value": 927,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_imi_vmx30ui_l0": {
  "value": 704,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, label 0",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_imi_vmx30ui_l1": {
  "value": 133,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, label 1",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_imi_vmx30ui_l2": {
  "value": 90,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui, label 2",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_l1": {
  "value": 465,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "all machines, label 1",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_l2_nondefective_tool": {
  "value": 0,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "label 2 on a tool without asterisk",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_tmf_vf10": {
  "value": 63,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_tmf_vf10_l0": {
  "value": 56,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10, label 0",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_tmf_vf10_l1": {
  "value": 7,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10, label 1",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_tmf_vf10_l2": {
  "value": 0,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10, label 2",
   "statistic": "count"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_paths_z_mismatch_table2": {
  "value": 0,
  "unit": "paths",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "all",
   "statistic": "paths whose flute count differs from the article's Table 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_quotes_total": {
  "value": 43,
  "unit": "quotes",
  "estimand": {
   "population": "quotes from S_article in inputs/sources.json",
   "filter": "none",
   "statistic": "count"
  },
  "source": "p00_sources.py",
  "kind": "value"
 },
 "n_quotes_verified": {
  "value": 43,
  "unit": "quotes",
  "estimand": {
   "population": "quotes from S_article in inputs/sources.json",
   "filter": "substring of page text after space normalisation",
   "statistic": "count"
  },
  "source": "p00_sources.py",
  "kind": "value"
 },
 "n_runs": {
  "value": 69,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "none",
   "statistic": "count of dataset folders"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_all_l1": {
  "value": 8,
  "unit": "runs",
  "estimand": {
   "population": "runs",
   "filter": "all machines",
   "statistic": "runs in which every path is label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_runs_all_l1_early_stopped": {
  "value": 5,
  "unit": "runs",
  "estimand": {
   "population": "runs",
   "filter": "all machines, runs in which every path is label 1",
   "statistic": "runs whose summary remark records an early stop"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_runs_all_l1_lab": {
  "value": 8,
  "unit": "runs",
  "estimand": {
   "population": "runs",
   "filter": "laboratory machines",
   "statistic": "runs in which every path is label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_runs_any_l1": {
  "value": 30,
  "unit": "runs",
  "estimand": {
   "population": "runs",
   "filter": "all machines",
   "statistic": "runs with at least one label 1 path"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_runs_any_l1_imi_vm20i": {
  "value": 19,
  "unit": "runs",
  "estimand": {
   "population": "runs",
   "filter": "imi_vm20i",
   "statistic": "runs with at least one label 1 path"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_runs_any_l1_imi_vmx30ui": {
  "value": 6,
  "unit": "runs",
  "estimand": {
   "population": "runs",
   "filter": "imi_vmx30ui",
   "statistic": "runs with at least one label 1 path"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_runs_any_l1_tmf_vf10": {
  "value": 5,
  "unit": "runs",
  "estimand": {
   "population": "runs",
   "filter": "tmf_vf10",
   "statistic": "runs with at least one label 1 path"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_runs_early_stop": {
  "value": 8,
  "unit": "runs",
  "estimand": {
   "population": "dataset_summary.xlsx",
   "filter": "Remark contains 'Early stopped'",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_half_l1_time_imi_vm20i": {
  "value": 3,
  "unit": "runs",
  "estimand": {
   "population": "runs",
   "filter": "imi_vm20i",
   "statistic": "fewest runs holding at least the cover share (l1_time_cover_share) of all label 1 cutting time"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_runs_half_l1_time_imi_vmx30ui": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "runs",
   "filter": "imi_vmx30ui",
   "statistic": "fewest runs holding at least the cover share (l1_time_cover_share) of all label 1 cutting time"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_runs_half_l1_time_tmf_vf10": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "runs",
   "filter": "tmf_vf10",
   "statistic": "fewest runs holding at least the cover share (l1_time_cover_share) of all label 1 cutting time"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_runs_imi_vm20i": {
  "value": 37,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i",
   "statistic": "count of dataset folders"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_imi_vmx30ui": {
  "value": 19,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui",
   "statistic": "count of dataset folders"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_one_stem": {
  "value": 69,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "none",
   "statistic": "runs whose sensor files share one reference timestamp stem"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_tmf_vf10": {
  "value": 13,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10",
   "statistic": "count of dataset folders"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_tool_mismatch_summary": {
  "value": 0,
  "unit": "runs",
  "estimand": {
   "population": "all runs",
   "filter": "none",
   "statistic": "runs whose label.csv tools differ from dataset_summary.xlsx"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_unsorted": {
  "value": 0,
  "unit": "runs",
  "estimand": {
   "population": "runs",
   "filter": "label.csv",
   "statistic": "runs whose rows are not in start order"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_runs_with_acc_imi_vm20i": {
  "value": 37,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i runs containing a acc file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_acc_imi_vmx30ui": {
  "value": 0,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui runs containing a acc file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_acc_tmf_vf10": {
  "value": 0,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10 runs containing a acc file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_ct_imi_vm20i": {
  "value": 0,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i runs containing a ct file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_ct_imi_vmx30ui": {
  "value": 0,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui runs containing a ct file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_ct_tmf_vf10": {
  "value": 13,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10 runs containing a ct file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_label_imi_vm20i": {
  "value": 37,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i runs containing a label file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_label_imi_vmx30ui": {
  "value": 19,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui runs containing a label file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_label_tmf_vf10": {
  "value": 13,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10 runs containing a label file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_mtc_imi_vm20i": {
  "value": 37,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i runs containing a mtc file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_mtc_imi_vmx30ui": {
  "value": 19,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui runs containing a mtc file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_mtc_tmf_vf10": {
  "value": 13,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10 runs containing a mtc file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_nc_imi_vm20i": {
  "value": 37,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i runs containing a nc file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_nc_imi_vmx30ui": {
  "value": 19,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui runs containing a nc file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_nc_tmf_vf10": {
  "value": 0,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10 runs containing a nc file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_remark": {
  "value": 14,
  "unit": "runs",
  "estimand": {
   "population": "dataset_summary.xlsx",
   "filter": "Remark not empty",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_s0_imi_vm20i": {
  "value": 37,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i runs containing a s0 file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_s0_imi_vmx30ui": {
  "value": 19,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui runs containing a s0 file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_s0_tmf_vf10": {
  "value": 13,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10 runs containing a s0 file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_s1_imi_vm20i": {
  "value": 37,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i runs containing a s1 file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_s1_imi_vmx30ui": {
  "value": 0,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui runs containing a s1 file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_s1_tmf_vf10": {
  "value": 0,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10 runs containing a s1 file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_s2_imi_vm20i": {
  "value": 37,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i runs containing a s2 file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_s2_imi_vmx30ui": {
  "value": 19,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui runs containing a s2 file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_s2_tmf_vf10": {
  "value": 13,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10 runs containing a s2 file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_video_imi_vm20i": {
  "value": 37,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vm20i runs containing a video file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_video_imi_vmx30ui": {
  "value": 19,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "imi_vmx30ui runs containing a video file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_with_video_tmf_vf10": {
  "value": 0,
  "unit": "runs",
  "estimand": {
   "population": "Kaggle release as received (folder archive (1)), Phase2 outputs excluded",
   "filter": "tmf_vf10 runs containing a video file",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_runs_wp_mismatch_summary": {
  "value": 0,
  "unit": "runs",
  "estimand": {
   "population": "all runs",
   "filter": "none",
   "statistic": "runs whose label.csv workpiece differs from dataset_summary.xlsx"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_summary_rows": {
  "value": 69,
  "unit": "rows",
  "estimand": {
   "population": "dataset_summary.xlsx",
   "filter": "none",
   "statistic": "rows"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_survivors": {
  "value": 3,
  "unit": "ideas",
  "estimand": {
   "population": "idea scores",
   "filter": "weighted score at or above the threshold",
   "statistic": "count"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "n_users_published": {
  "value": 2,
  "unit": "users",
  "estimand": {
   "population": "retrieved works using the dataset",
   "filter": "with a DOI",
   "statistic": "count"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "n_users_repo": {
  "value": 1,
  "unit": "users",
  "estimand": {
   "population": "retrieved works and repositories using the dataset",
   "filter": "without a DOI (code repositories)",
   "statistic": "count"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "n_video_negative_offset": {
  "value": 31,
  "unit": "runs",
  "estimand": {
   "population": "lab runs with video",
   "filter": "all",
   "statistic": "runs whose video starts before the reference stem"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "n_zero_gaps": {
  "value": 12,
  "unit": "pairs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "consecutive paths within a run",
   "statistic": "pairs with no gap (end equals next start)"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_zero_gaps_label_change": {
  "value": 12,
  "unit": "pairs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "consecutive paths within a run",
   "statistic": "pairs with no gap whose two labels differ"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "n_zero_gaps_tmf_vf10": {
  "value": 12,
  "unit": "pairs",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "consecutive paths within a tmf_vf10 run",
   "statistic": "pairs with no gap"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "near_band": {
  "value": 0.15,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "band below the threshold called near-survivor",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "ovr_imi_vmx30ui_run19_len_ratio": {
  "value": 0.666210937499994,
  "unit": "ratio",
  "estimand": {
   "population": "laboratory run",
   "filter": "imi_vmx30ui dataset19, paths matched on tool, workpiece, F and ae to runs at 100 percent override",
   "statistic": "median ratio of duration times F to the matched median"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "ovr_imi_vmx30ui_run19_matched": {
  "value": 64,
  "unit": "paths",
  "estimand": {
   "population": "laboratory run",
   "filter": "imi_vmx30ui dataset19",
   "statistic": "paths with a matched reference"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "ovr_imi_vmx30ui_run19_paths": {
  "value": 64,
  "unit": "paths",
  "estimand": {
   "population": "laboratory run",
   "filter": "imi_vmx30ui dataset19",
   "statistic": "paths"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "ovr_imi_vmx30ui_run19_pct": {
  "value": 150.0,
  "unit": "percent",
  "estimand": {
   "population": "laboratory run",
   "filter": "imi_vmx30ui dataset19",
   "statistic": "median time-weighted feed override over the run's paths"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "pairs_n": {
  "value": 1142,
  "unit": "pairs",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "laboratory machines, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "count"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "pairs_up_first_share": {
  "value": 0.9947460595446584,
  "unit": "fraction",
  "estimand": {
   "population": "design pairs of up and down paths",
   "filter": "laboratory machines, same run, tool, ap, ae, N and F, labels 0 or 1",
   "statistic": "share of pairs in which the up path starts first"
  },
  "source": "p03_stats.py",
  "kind": "value"
 },
 "path_share_imi_vm20i_l0": {
  "value": 0.7400630914826498,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "share of paths with label 0"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "path_share_imi_vm20i_l1": {
  "value": 0.20504731861198738,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "path_share_imi_vm20i_l2": {
  "value": 0.05488958990536277,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "path_share_imi_vmx30ui_l0": {
  "value": 0.7594390507011867,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "share of paths with label 0"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "path_share_imi_vmx30ui_l1": {
  "value": 0.14347357065803668,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "path_share_imi_vmx30ui_l2": {
  "value": 0.0970873786407767,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "path_share_tmf_vf10_l0": {
  "value": 0.8888888888888888,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10",
   "statistic": "share of paths with label 0"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "path_share_tmf_vf10_l1": {
  "value": 0.1111111111111111,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10",
   "statistic": "share of paths with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "path_share_tmf_vf10_l2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10",
   "statistic": "share of paths with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "pkg_s1_n": {
  "value": 3,
  "unit": "ideas",
  "estimand": {
   "population": "packages",
   "filter": "S1",
   "statistic": "member ideas"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "pkg_s2_n": {
  "value": 2,
  "unit": "ideas",
  "estimand": {
   "population": "packages",
   "filter": "S2",
   "statistic": "member ideas"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "pmiss_i01": {
  "value": 0.2,
  "unit": "probability",
  "estimand": {
   "population": "analyst judgment",
   "filter": "probability that close prior work for I01 was missed",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "pmiss_s1": {
  "value": 0.3,
  "unit": "probability",
  "estimand": {
   "population": "analyst judgment",
   "filter": "probability that close prior work for S1 was missed",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "pmiss_s2": {
  "value": 0.4,
  "unit": "probability",
  "estimand": {
   "population": "analyst judgment",
   "filter": "probability that close prior work for S2 was missed",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "prespec_sha256": {
  "value": "1d77032d8750f4c49f9b98580d28acd89a27eb6a4b8d194cb6553e7ebec2d9a7",
  "unit": "sha256",
  "estimand": {
   "population": "outputs/prespec_descriptive_questions.md",
   "filter": "file as frozen",
   "statistic": "sha256"
  },
  "source": "p05_ideas.py",
  "kind": "text"
 },
 "prespec_time": {
  "value": "2026-09-22T19:44:10Z",
  "unit": "UTC time",
  "estimand": {
   "population": "outputs/prespec_descriptive_questions.md",
   "filter": "file as frozen",
   "statistic": "time the hash was taken"
  },
  "source": "p05_ideas.py",
  "kind": "text"
 },
 "q_databases": {
  "value": 7,
  "unit": "databases",
  "estimand": {
   "population": "inputs/query_log.tsv",
   "filter": "none",
   "statistic": "distinct databases queried"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "q_openalex_answered": {
  "value": 61,
  "unit": "queries",
  "estimand": {
   "population": "inputs/query_log.tsv",
   "filter": "OpenAlex batches A to C",
   "statistic": "queries answered"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "q_openalex_refused": {
  "value": 35,
  "unit": "queries",
  "estimand": {
   "population": "inputs/query_log.tsv",
   "filter": "OpenAlex re-runs",
   "statistic": "queries refused with HTTP 429"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "q_rows": {
  "value": 83,
  "unit": "entries",
  "estimand": {
   "population": "inputs/query_log.tsv",
   "filter": "none",
   "statistic": "entries"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_clip_random": {
  "value": 0.9905895468555315,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set clip",
   "statistic": "median over repeats of out-of-fold AUC, five folds, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_clip_random_max": {
  "value": 0.9907613614007476,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set clip",
   "statistic": "max over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_clip_random_min": {
  "value": 0.9904282248016264,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set clip",
   "statistic": "min over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_clip_run": {
  "value": 0.9849262246704702,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set clip",
   "statistic": "median over repeats of out-of-fold AUC, five folds, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_clip_run_max": {
  "value": 0.9875900059020264,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set clip",
   "statistic": "max over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_clip_run_min": {
  "value": 0.9773991737163092,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set clip",
   "statistic": "min over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_highrate_random": {
  "value": 0.9950173781887337,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set highrate",
   "statistic": "median over repeats of out-of-fold AUC, five folds, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_highrate_random_max": {
  "value": 0.9952600170502984,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set highrate",
   "statistic": "max over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_highrate_random_min": {
  "value": 0.9946330906944717,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set highrate",
   "statistic": "min over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_highrate_run": {
  "value": 0.9921122696570267,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set highrate",
   "statistic": "median over repeats of out-of-fold AUC, five folds, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_highrate_run_max": {
  "value": 0.9937779526526329,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set highrate",
   "statistic": "max over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_highrate_run_min": {
  "value": 0.989415699390124,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set highrate",
   "statistic": "min over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_mtc_random": {
  "value": 0.9803580562659846,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set mtc",
   "statistic": "median over repeats of out-of-fold AUC, five folds, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_mtc_random_max": {
  "value": 0.981131877500164,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set mtc",
   "statistic": "max over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_mtc_random_min": {
  "value": 0.97905961046626,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set mtc",
   "statistic": "min over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_mtc_run": {
  "value": 0.9727903469079939,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set mtc",
   "statistic": "median over repeats of out-of-fold AUC, five folds, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_mtc_run_max": {
  "value": 0.9773886812249982,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set mtc",
   "statistic": "max over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_mtc_run_min": {
  "value": 0.9598426126303363,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set mtc",
   "statistic": "min over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_s2only_random": {
  "value": 0.8232920191487967,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set s2only",
   "statistic": "median over repeats of out-of-fold AUC, five folds, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_s2only_random_max": {
  "value": 0.8240409207161126,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set s2only",
   "statistic": "max over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_s2only_random_min": {
  "value": 0.8221339104203556,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set s2only",
   "statistic": "min over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_s2only_run": {
  "value": 0.7781362712309003,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set s2only",
   "statistic": "median over repeats of out-of-fold AUC, five folds, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_s2only_run_max": {
  "value": 0.8135746606334842,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set s2only",
   "statistic": "max over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vm20i_s2only_run_min": {
  "value": 0.6969453734671125,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set s2only",
   "statistic": "min over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_clip_random": {
  "value": 0.7935935363978127,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set clip",
   "statistic": "median over repeats of out-of-fold AUC, five folds, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_clip_random_max": {
  "value": 0.8151646872863977,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set clip",
   "statistic": "max over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_clip_random_min": {
  "value": 0.7690212747778538,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set clip",
   "statistic": "min over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_clip_run": {
  "value": 0.480001495215311,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set clip",
   "statistic": "median over repeats of out-of-fold AUC, five folds, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_clip_run_max": {
  "value": 0.5738956766917294,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set clip",
   "statistic": "max over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_clip_run_min": {
  "value": 0.16883116883116886,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set clip",
   "statistic": "min over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_highrate_random": {
  "value": 0.8507988721804511,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set highrate",
   "statistic": "median over repeats of out-of-fold AUC, five folds, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_highrate_random_max": {
  "value": 0.852497009569378,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set highrate",
   "statistic": "max over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_highrate_random_min": {
  "value": 0.8474773581681477,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set highrate",
   "statistic": "min over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_highrate_run": {
  "value": 0.7546672077922079,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set highrate",
   "statistic": "median over repeats of out-of-fold AUC, five folds, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_highrate_run_max": {
  "value": 0.8203712406015037,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set highrate",
   "statistic": "max over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_highrate_run_min": {
  "value": 0.4040926179084074,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set highrate",
   "statistic": "min over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_mtc_random": {
  "value": 0.9531036397812714,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set mtc",
   "statistic": "median over repeats of out-of-fold AUC, five folds, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_mtc_random_max": {
  "value": 0.9574397641831852,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set mtc",
   "statistic": "max over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_mtc_random_min": {
  "value": 0.9471548188653452,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set mtc",
   "statistic": "min over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_mtc_run": {
  "value": 0.9231619531784006,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set mtc",
   "statistic": "median over repeats of out-of-fold AUC, five folds, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_mtc_run_max": {
  "value": 0.9366669514695831,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set mtc",
   "statistic": "max over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_mtc_run_min": {
  "value": 0.8045219583048528,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set mtc",
   "statistic": "min over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_s2only_random": {
  "value": 0.7869371582365003,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set s2only",
   "statistic": "median over repeats of out-of-fold AUC, five folds, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_s2only_random_max": {
  "value": 0.7883843130553656,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set s2only",
   "statistic": "max over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_s2only_random_min": {
  "value": 0.7835141831852358,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set s2only",
   "statistic": "min over repeats, random split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_s2only_run": {
  "value": 0.7054212235133288,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set s2only",
   "statistic": "median over repeats of out-of-fold AUC, five folds, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_s2only_run_max": {
  "value": 0.7495193950786055,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set s2only",
   "statistic": "max over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_auc_imi_vmx30ui_s2only_run_min": {
  "value": 0.40658108339029386,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set s2only",
   "statistic": "min over repeats, run split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_folds": {
  "value": 5,
  "unit": "folds",
  "estimand": {
   "population": "screening setting",
   "filter": "p04_screen.py",
   "statistic": "folds per split"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_gap_imi_vm20i_clip": {
  "value": 0.00566332218506127,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set clip",
   "statistic": "median random-split AUC minus median run-split AUC"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_gap_imi_vm20i_highrate": {
  "value": 0.0029051085317070013,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set highrate",
   "statistic": "median random-split AUC minus median run-split AUC"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_gap_imi_vm20i_mtc": {
  "value": 0.007567709357990671,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set mtc",
   "statistic": "median random-split AUC minus median run-split AUC"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_gap_imi_vm20i_s2only": {
  "value": 0.04515574791789634,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1, feature set s2only",
   "statistic": "median random-split AUC minus median run-split AUC"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_gap_imi_vmx30ui_clip": {
  "value": 0.3135920411825017,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set clip",
   "statistic": "median random-split AUC minus median run-split AUC"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_gap_imi_vmx30ui_highrate": {
  "value": 0.09613166438824328,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set highrate",
   "statistic": "median random-split AUC minus median run-split AUC"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_gap_imi_vmx30ui_mtc": {
  "value": 0.029941686602870887,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set mtc",
   "statistic": "median random-split AUC minus median run-split AUC"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_gap_imi_vmx30ui_s2only": {
  "value": 0.08151593472317153,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1, feature set s2only",
   "statistic": "median random-split AUC minus median run-split AUC"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_nfeat_imi_vm20i_clip": {
  "value": 1,
  "unit": "features",
  "estimand": {
   "population": "screening setting",
   "filter": "imi_vm20i, feature set clip",
   "statistic": "features available and used"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_nfeat_imi_vm20i_highrate": {
  "value": 5,
  "unit": "features",
  "estimand": {
   "population": "screening setting",
   "filter": "imi_vm20i, feature set highrate",
   "statistic": "features available and used"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_nfeat_imi_vm20i_mtc": {
  "value": 6,
  "unit": "features",
  "estimand": {
   "population": "screening setting",
   "filter": "imi_vm20i, feature set mtc",
   "statistic": "features available and used"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_nfeat_imi_vm20i_s2only": {
  "value": 1,
  "unit": "features",
  "estimand": {
   "population": "screening setting",
   "filter": "imi_vm20i, feature set s2only",
   "statistic": "features available and used"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_nfeat_imi_vmx30ui_clip": {
  "value": 1,
  "unit": "features",
  "estimand": {
   "population": "screening setting",
   "filter": "imi_vmx30ui, feature set clip",
   "statistic": "features available and used"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_nfeat_imi_vmx30ui_highrate": {
  "value": 2,
  "unit": "features",
  "estimand": {
   "population": "screening setting",
   "filter": "imi_vmx30ui, feature set highrate",
   "statistic": "features available and used"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_nfeat_imi_vmx30ui_mtc": {
  "value": 6,
  "unit": "features",
  "estimand": {
   "population": "screening setting",
   "filter": "imi_vmx30ui, feature set mtc",
   "statistic": "features available and used"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_nfeat_imi_vmx30ui_s2only": {
  "value": 1,
  "unit": "features",
  "estimand": {
   "population": "screening setting",
   "filter": "imi_vmx30ui, feature set s2only",
   "statistic": "features available and used"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s1_repeats": {
  "value": 20,
  "unit": "repeats",
  "estimand": {
   "population": "screening setting",
   "filter": "p04_screen.py",
   "statistic": "repeated random splits per scheme"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s3_auc_toola_imi_vm20i": {
  "value": 0.8218820906288937,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vm20i, labels 0 and 1",
   "statistic": "AUC of the indicator tool A or A* (ties count half)"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "s3_auc_toola_imi_vmx30ui": {
  "value": 0.9101055194805194,
  "unit": "AUC",
  "estimand": {
   "population": "labelled cutting paths",
   "filter": "imi_vmx30ui, labels 0 and 1",
   "statistic": "AUC of the indicator tool A or A* (ties count half)"
  },
  "source": "p04_screen.py",
  "kind": "value"
 },
 "sc_i01_audience": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I01, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i01_cost": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I01, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i01_desk_risk": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I01, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i01_effect": {
  "value": 5,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I01, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i01_feasibility": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I01, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i01_novelty": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I01, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i02_audience": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I02, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i02_cost": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I02, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i02_desk_risk": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I02, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i02_effect": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I02, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i02_feasibility": {
  "value": 5,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I02, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i02_novelty": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I02, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i03_audience": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I03, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i03_cost": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I03, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i03_desk_risk": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I03, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i03_effect": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I03, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i03_feasibility": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I03, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i03_novelty": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I03, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i04_audience": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I04, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i04_cost": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I04, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i04_desk_risk": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I04, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i04_effect": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I04, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i04_feasibility": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I04, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i04_novelty": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I04, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i05_audience": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I05, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i05_cost": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I05, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i05_desk_risk": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I05, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i05_effect": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I05, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i05_feasibility": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I05, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i05_novelty": {
  "value": 1,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I05, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i06_audience": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I06, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i06_cost": {
  "value": 5,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I06, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i06_desk_risk": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I06, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i06_effect": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I06, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i06_feasibility": {
  "value": 5,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I06, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i06_novelty": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I06, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i07_audience": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I07, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i07_cost": {
  "value": 5,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I07, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i07_desk_risk": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I07, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i07_effect": {
  "value": 5,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I07, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i07_feasibility": {
  "value": 5,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I07, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i07_novelty": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I07, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i08_audience": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I08, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i08_cost": {
  "value": 5,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I08, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i08_desk_risk": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I08, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i08_effect": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I08, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i08_feasibility": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I08, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i08_novelty": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I08, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i09_audience": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I09, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i09_cost": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I09, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i09_desk_risk": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I09, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i09_effect": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I09, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i09_feasibility": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I09, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i09_novelty": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I09, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i10_audience": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I10, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i10_cost": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I10, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i10_desk_risk": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I10, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i10_effect": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I10, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i10_feasibility": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I10, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i10_novelty": {
  "value": 1,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I10, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i11_audience": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I11, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i11_cost": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I11, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i11_desk_risk": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I11, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i11_effect": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I11, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i11_feasibility": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I11, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i11_novelty": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I11, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i12_audience": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I12, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i12_cost": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I12, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i12_desk_risk": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I12, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i12_effect": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I12, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i12_feasibility": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I12, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i12_novelty": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I12, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i13_audience": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I13, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i13_cost": {
  "value": 5,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I13, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i13_desk_risk": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I13, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i13_effect": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I13, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i13_feasibility": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I13, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i13_novelty": {
  "value": 1,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I13, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i14_audience": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I14, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i14_cost": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I14, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i14_desk_risk": {
  "value": 1,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I14, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i14_effect": {
  "value": 1,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I14, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i14_feasibility": {
  "value": 1,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I14, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i14_novelty": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I14, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i15_audience": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I15, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i15_cost": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I15, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i15_desk_risk": {
  "value": 1,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I15, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i15_effect": {
  "value": 1,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I15, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i15_feasibility": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I15, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i15_novelty": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I15, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i16_audience": {
  "value": 1,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I16, audience",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i16_cost": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I16, cost",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i16_desk_risk": {
  "value": 1,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I16, desk_risk",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i16_effect": {
  "value": 2,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I16, effect",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i16_feasibility": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I16, feasibility",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sc_i16_novelty": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "I16, novelty",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "score_max": {
  "value": 5,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "rubric scale",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "score_min": {
  "value": 1,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "rubric scale",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "sources_retrieved": {
  "value": "2026-09-22",
  "unit": "date",
  "estimand": {
   "population": "external source",
   "filter": "S_article",
   "statistic": "value as printed",
   "retrieved": "2026-09-22"
  },
  "source": "p00_sources.py",
  "kind": "external"
 },
 "summary_matches_folders": {
  "value": 1,
  "unit": "boolean",
  "estimand": {
   "population": "dataset_summary.xlsx vs dataset folders",
   "filter": "none",
   "statistic": "1 if the (machine, dataset) sets are equal"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "t4_max_abs_dev_pp": {
  "value": 0.0,
  "unit": "percentage points",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "machine by label cells",
   "statistic": "max absolute difference between time shares rounded to 2 decimals and Table 4"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "threshold": {
  "value": 3.5,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "survivor threshold on the weighted score",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "time_share_imi_vm20i_l0": {
  "value": 0.6916763278290207,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "share of cutting time with label 0"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "time_share_imi_vm20i_l1": {
  "value": 0.26329547819826027,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "time_share_imi_vm20i_l2": {
  "value": 0.04502819397271907,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "share of cutting time with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "time_share_imi_vmx30ui_l0": {
  "value": 0.6448468360766887,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "share of cutting time with label 0"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "time_share_imi_vmx30ui_l1": {
  "value": 0.27711148238345573,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "time_share_imi_vmx30ui_l2": {
  "value": 0.07804168153985555,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "share of cutting time with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "time_share_tmf_vf10_l0": {
  "value": 0.9748622550037827,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10",
   "statistic": "share of cutting time with label 0"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "time_share_tmf_vf10_l1": {
  "value": 0.025137744996217376,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10",
   "statistic": "share of cutting time with label 1"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "time_share_tmf_vf10_l2": {
  "value": 0.0,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "tmf_vf10",
   "statistic": "share of cutting time with label 2"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "tot_i01": {
  "value": 3.2,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I01",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i02": {
  "value": 3.55,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I02",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i03": {
  "value": 3.45,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I03",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i04": {
  "value": 2.75,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I04",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i05": {
  "value": 2.15,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I05",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i06": {
  "value": 3.9,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I06",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i07": {
  "value": 3.85,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I07",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i08": {
  "value": 3.4,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I08",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i09": {
  "value": 2.2,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I09",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i10": {
  "value": 2.2,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I10",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i11": {
  "value": 3.05,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I11",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i12": {
  "value": 2.25,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I12",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i13": {
  "value": 2.25,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I13",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i14": {
  "value": 1.7,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I14",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i15": {
  "value": 1.9,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I15",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "tot_i16": {
  "value": 2.55,
  "unit": "score",
  "estimand": {
   "population": "idea scores",
   "filter": "I16",
   "statistic": "weighted sum of the six criterion scores"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "vet_documentation": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "validity component documentation",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "vet_errata": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "validity component errata",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "vet_licensing": {
  "value": 5,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "validity component licensing",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "vet_mean": {
  "value": 3.8,
  "unit": "score",
  "estimand": {
   "population": "validity components",
   "filter": "all five",
   "statistic": "unweighted mean of the component judgments"
  },
  "source": "p05_ideas.py",
  "kind": "value"
 },
 "vet_provenance": {
  "value": 4,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "validity component provenance",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "vet_size": {
  "value": 3,
  "unit": "score",
  "estimand": {
   "population": "analyst judgment",
   "filter": "validity component size",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "vf10_max_f_per_tool_run": {
  "value": 1,
  "unit": "feeds",
  "estimand": {
   "population": "tmf_vf10 runs",
   "filter": "label.csv",
   "statistic": "most distinct F values for one tool within one run"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "vf10_runs_feed_turned_down": {
  "value": 2,
  "unit": "runs",
  "estimand": {
   "population": "dataset_summary.xlsx",
   "filter": "tmf_vf10",
   "statistic": "runs whose remark records a feed reduction"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "vf10_runs_multiple_f": {
  "value": 13,
  "unit": "runs",
  "estimand": {
   "population": "tmf_vf10 runs",
   "filter": "label.csv",
   "statistic": "runs with more than one feed value"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "vf10_tools_per_run_max": {
  "value": 2,
  "unit": "tools",
  "estimand": {
   "population": "tmf_vf10 runs",
   "filter": "label.csv",
   "statistic": "most distinct tools in one run"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "vf10_tools_per_run_min": {
  "value": 2,
  "unit": "tools",
  "estimand": {
   "population": "tmf_vf10 runs",
   "filter": "label.csv",
   "statistic": "fewest distinct tools in one run"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "video_offset_max_s": {
  "value": 21.305405,
  "unit": "s",
  "estimand": {
   "population": "lab runs with video",
   "filter": "all",
   "statistic": "max of video file-name time minus reference stem time"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "video_offset_median_s": {
  "value": -5.6064050000000005,
  "unit": "s",
  "estimand": {
   "population": "lab runs with video",
   "filter": "all",
   "statistic": "median of video file-name time minus reference stem time"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "video_offset_min_s": {
  "value": -143.523,
  "unit": "s",
  "estimand": {
   "population": "lab runs with video",
   "filter": "all",
   "statistic": "min of video file-name time minus reference stem time"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "w_audience": {
  "value": 0.15,
  "unit": "weight",
  "estimand": {
   "population": "analyst judgment",
   "filter": "rubric weights",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "w_cost": {
  "value": 0.1,
  "unit": "weight",
  "estimand": {
   "population": "analyst judgment",
   "filter": "rubric weights",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "w_desk_risk": {
  "value": 0.15,
  "unit": "weight",
  "estimand": {
   "population": "analyst judgment",
   "filter": "rubric weights",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "w_effect": {
  "value": 0.15,
  "unit": "weight",
  "estimand": {
   "population": "analyst judgment",
   "filter": "rubric weights",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "w_feasibility": {
  "value": 0.2,
  "unit": "weight",
  "estimand": {
   "population": "analyst judgment",
   "filter": "rubric weights",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "w_novelty": {
  "value": 0.25,
  "unit": "weight",
  "estimand": {
   "population": "analyst judgment",
   "filter": "rubric weights",
   "statistic": "judgment"
  },
  "source": "p05_ideas.py",
  "kind": "judgment"
 },
 "watt_single_value_share_imi_vm20i": {
  "value": 0.005047318611987382,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vm20i",
   "statistic": "share of paths in which true power takes one value"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "watt_single_value_share_imi_vmx30ui": {
  "value": 0.025889967637540454,
  "unit": "fraction",
  "estimand": {
   "population": "labelled cutting paths (label.csv rows)",
   "filter": "imi_vmx30ui",
   "statistic": "share of paths in which true power takes one value"
  },
  "source": "p02_profile.py",
  "kind": "value"
 },
 "wav_all_48k_16bit_mono": {
  "value": 1,
  "unit": "boolean",
  "estimand": {
   "population": "WAV files",
   "filter": "all",
   "statistic": "1 if every header is 48 kHz, 16-bit, mono, uncompressed"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "wav_clip_samples_total": {
  "value": 34117848,
  "unit": "samples",
  "estimand": {
   "population": "WAV files",
   "filter": "all",
   "statistic": "full-scale samples"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "wav_n": {
  "value": 175,
  "unit": "files",
  "estimand": {
   "population": "WAV files",
   "filter": "all",
   "statistic": "count"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "wav_n_files_with_clipping": {
  "value": 132,
  "unit": "files",
  "estimand": {
   "population": "WAV files",
   "filter": "all",
   "statistic": "files with at least one full-scale sample"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 },
 "wav_samples_total": {
  "value": 3402051836,
  "unit": "samples",
  "estimand": {
   "population": "WAV files",
   "filter": "all",
   "statistic": "samples"
  },
  "source": "p01_inventory.py",
  "kind": "value"
 }
}