Agarwal et al. (2025) Joint Library Dataset Evaluator Container for the Genomic API for Model Evaluation (GAME)
Bibliographic record
Abstract
This container is the Evaluator configured for the Genomic API for Model Evaluation (GAME), designed specifically to evaluate model predictions against the Agarwal et al. (2025) Joint Library dataset. The dataset consists of approximately 60,000 candidate cis-regulatory elements (cCREs), including enhancers and promoters systematically tested across HepG2, K562, and WTC11 cell lines, along with positive and negative control sequences. Each element is represented by a 230-bp oligonucleotide, enabling standardized model benchmarking. Agarwal, V., Inoue, F., Schubach, M. et al. Massively parallel characterization of transcriptional regulatory elements. Nature (2025). https://doi.org/10.1038/s41586-024-08430-9 This container includes: Evaluator API script for genomic sequence evaluation. Evaluator helper script for error checking functions. Installed Python dependencies required by the Evaluator. Additional information can be found on GitHub: Genomic API for Model Evaluation Evaluator-specific information can be found within the same repository: Agarwal et al. (2025)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.066 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".