You are screening bibliographic records for a meta-research study of machine learning (ML)
applied to pharmaceutical manufacturing and quality control. Read the instructions, then
screen every record in {BATCH_FILE} (JSON Lines; fields: key, title, abstract, venue, dates,
pubtypes). Write one JSON object per record to {OUTPUT_FILE} with fields:
key, decision ("include", "exclude" or "unclear"), reason (one code below), note (at most 15
words, optional).

INCLUDE only if ALL of these hold on the title and abstract:
 I1. Primary research that fits or evaluates at least one machine-learning model on data
     (supervised or unsupervised learning: neural networks of any kind, tree ensembles,
     random forests, gradient boosting, support vector machines or regression, k-nearest
     neighbors, Gaussian process regression, decision trees, or clustering used for
     prediction). Classical chemometrics alone (PLS, PCA, PCR, MLR, DoE response surfaces)
     does NOT qualify unless an ML model of the kind listed is also fitted.
 I2. The data are generated by the MANUFACTURE or QUALITY CONTROL of a human drug product,
     drug substance or biologic (small molecule, therapeutic protein, monoclonal antibody,
     vaccine): process development or commercial processing, unit operations (e.g.,
     granulation, blending, compression, coating, drying, crystallization, lyophilization,
     spray drying, extrusion, cell culture/bioreactor, purification), process analytical
     technology, in-process controls, release or stability testing, or analytical methods
     applied to drug products or substances for quality control.
 I3. At least one quantitative performance result of the ML model is reported or evidently
     reported (e.g., accuracy, R2, RMSE, AUC).

EXCLUDE with one reason code:
 E1_no_ml       no ML model fitted (review of ML, chemometrics only, mechanistic model only)
 E2_domain      not pharmaceutical manufacturing or QC (e.g., food, cosmetics, chemicals,
                agriculture, environment, devices, education, general engineering)
 E3_discovery   drug discovery, QSAR, ADMET, molecular design, target identification
 E4_clinical    clinical prediction, pharmacokinetics, bioequivalence, pharmacovigilance,
                hospital or pharmacy practice, medication management, point-of-care printing
 E5_supply      supply chain, demand forecasting, sales, commercial operations
 E6_herbal      herbal, traditional-medicine or natural-product raw-material authentication
 E7_falsified   falsified or counterfeit medicine detection by market-surveillance testing
 E8_genetx      gene-therapy vector sequence or genome characterization
 E9_property    physicochemical property prediction (e.g., solubility) without process or
                product quality data
 E10_type       review, perspective, editorial, tutorial, protocol, preprint, thesis, book
                chapter, conference abstract without results
 E11_no_result  ML is proposed or described but no performance result is reported
 E12_other      other (explain in note)

Use "unclear" (reason U_needs_fulltext) only when the title and abstract do not settle I1,
I2 or I3 and the full text would. A missing abstract is not by itself a reason to include.
Decide each record independently. Do not use any knowledge about a record beyond its fields.
