You are scoring published articles for a meta-research audit of how machine-learning (ML)
studies in pharmaceutical manufacturing and quality control are reported. {BATCH_FILE} lists
a batch of articles, each with the path of its full-text file. Score each article separately,
from its own full-text file only, and write one JSON object per article, one per line, to
{OUTPUT_FILE}. Never infer an item from general knowledge, from the journal, from another
article, or from what authors usually do.

Step 1. Headline result. Identify the headline result: the first quantitative performance
result of the proposed ML model stated in the abstract; if the abstract states none, the best
model's main performance result in the Results. Record it (at most 15 words, quoted).

Step 2. Eligibility check on the full text. If the article does not meet the screening
criteria I1-I3 (see the screening prompt), record eligible = false with the exclusion code,
and stop.

Step 3. Score items R1-R9 for the model and data behind the headline result. Each item takes
exactly one value:
  Y   = the full text reports it (quote the evidence),
  N   = the full text does not report it, or reports its absence (quote the statement if one
        exists; otherwise evidence = "not reported" and list the search terms used),
  NA  = not applicable (only where the item says so),
  NASS = not assessable: the evidence is in a part of the article that could not be reached
        (for example a supplementary file or a linked page that did not open). Never use NASS
        for something that is simply not written in the text.
Every Y and every N-with-statement carries a locator (section heading, table or figure
number, or page) and a verbatim quote of at most 15 words.

R1 Data availability. Y if the data behind the headline result are publicly accessible: a
   repository link or DOI, a supplementary data file, data printed in the article in full, or
   a cited public dataset that was used as is. N if "available on request", "confidential",
   "proprietary", or no statement.
R2 Code availability. Y if the analysis code is publicly accessible (repository link, DOI or
   supplementary code file). Naming software, libraries or versions is not code
   availability. N otherwise, including "on request".
R3 Data split stated. Y if the text states how data were partitioned for evaluation (hold-out
   proportions or counts, k-fold or leave-one-out cross-validation, split by batch, time or
   site). N otherwise.
R4 Held-out evaluation. Y if the headline result is computed on data not used to fit the model
   or to select it or its hyperparameters (an untouched test set, an external set, or nested
   cross-validation). N if it comes from training data, from the same cross-validation used for
   tuning or model selection, or if the text is silent on this.
R5 Uncertainty. Y if the headline performance metric is reported with a confidence interval,
   standard deviation, standard error or range over repeats. N otherwise.
R6 Baseline comparison. Y if the proposed model is compared on the same data with at least one
   simpler or incumbent method (a linear, PLS or other classical model, the current practice,
   a null or mean predictor, or a previously published method). Comparing only several complex
   ML models with each other is N.
R7 Class distribution. For a classification task: Y if class counts or proportions are reported
   for the data used; N otherwise. For regression or other non-classification tasks: NA.
R8 External validation. Y if the model is evaluated on data from a different batch campaign,
   scale, site, instrument, time period, or an independent dataset not drawn from the
   development data. A random split of one dataset is N.
R9 Repeated evaluation. Y if performance is evaluated over more than one random split, seed or
   resample (repeated cross-validation, bootstrap, multiple runs). A single k-fold
   cross-validation counts as Y only if fold-level results or their spread are reported.

Step 4. Descriptors: task (classification, regression, clustering, other); ML family (neural
network, tree ensemble, SVM/SVR, Gaussian process, k-NN, other); data provenance (industrial,
laboratory or pilot, public dataset, simulated, not stated).

Output one JSON object with fields: key, eligible, exclusion_code, headline, R1..R9 (each an
object {value, locator, quote}), task, ml_family, provenance, notes (at most 30 words).
