IARC Workshop on the Key Characteristics of Carcinogens: Assessment of End Points for Evaluating Mechanistic Evidence of Carcinogenic Hazards
Bibliographic record
Abstract
BACKGROUND: programme has successfully applied the KCs framework for the mechanistic evaluation of different types of exposures, including chemicals, metals, and complex exposures, such as environmental, occupational, or dietary exposures. The use of this framework has significantly enhanced the identification and organization of relevant mechanistic data, minimized bias in evaluations, and enriched the knowledge base regarding the mechanisms of known and suspected carcinogens. OBJECTIVES: evaluations. METHODS: ) the integration of the mechanistic evidence as part of cancer hazard identification. The workshop participants assessed the relevance and the informativeness of multiple KCs-associated end points for the evaluation of mechanistic evidence in studies of exposed humans and experimental systems. DISCUSSION: or in other contexts. https://doi.org/10.1289/EHP15389.
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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 | 0.181 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".