# Online Resource 2: frozen reproducibility archive

Article: *Pretraining Transfer, Adaptation Dependence, and Dense Evaluation in Low-Label Cell Instance Segmentation: A Controlled Study*

Author: Yinghuan Li, Nanjing University of Information Science and Technology, yinghuanli763@gmail.com

## Scope

This archive contains machine-readable tables and the code paths needed to audit the manuscript's numerical claims and regenerate its figures. The public code repository is https://github.com/Ehowtan/cell-ssl-seg. This frozen archive does not redistribute LIVECell or Cellpose images, COCO/ImageNet weights, SSL checkpoints, full model checkpoints, or large prediction files. Those assets remain governed by their original licenses and are identified by configuration records and SHA-256 hashes in the project audit.

Public repository HEAD verified at packaging: `63b85910f9265d573403441ed2c667fae0e7a218`.

## Contents

- `tables/`: frozen CSV tables used by the manuscript
- `evidence/`: evidence registry, manuscript audit, split manifest, qualitative-selection audits, and prediction-count table
- `analysis/`: evidence-table builder, figure generator, and package validator
- `code/scripts/`: formal training, SSL transfer, dense evaluation, and protocol helpers in their repository-relative layout
- `figures/`: publication figures in PDF format

## Evidence boundaries

- The official LIVECell endpoint uses three training seeds.
- The matched-random-component control uses two seeds; the 100-epoch factorial uses seeds 42/43/44.
- The paired image bootstrap conditions on the fixed seed-42/43 models and does not estimate training randomness.
- The Cellpose table is a supplementary capped protocol diagnostic, not main external-validation evidence. It contains a three-seed ImageNet-versus-ImageNet+COCO paired component comparison and a separate three-seed COCO-full reference.
- Legacy cross-cell-line, full-data, freezing, and representation results are exploratory.
- MS-CLACL quantitative results are excluded because of annotation leakage and an unmatched pretraining budget.
- BBBC039 was downloaded but not evaluated.

## Regeneration order

1. Install the versions listed in `requirements.txt` or compatible later versions.
2. Place the audited result summaries at the relative paths documented by the evidence registry.
3. Run `analysis/build_evidence_tables.py`.
4. Run `analysis/make_mva_figures.py`.
5. Run `analysis/validate_mva_package.py` from the complete manuscript workspace.

The formal maxDets-only evaluator has a fail-closed gate: its AP at the largest cap must reproduce the pre-existing formal AP within the stated tolerance before lower-cap values are accepted.

The archive intentionally omits machine-specific absolute paths from raw audit records. The unmodified project history and current source are available in the public repository above.
