Development of a database on tumors and tumor sites in humans and in experimental animals for 'Group 1 agents identified through volume 109 of the <i>IARC Monographs</i>
Bibliographic record
Abstract
Volume 100 in the series of IARC Monographs on the Evaluation of Carcinogenic Risks to Humans comprises an update and review of relevant information on all agents determined to induce cancer in humans. These Group 1 agents are categorized in 6 Monographs (Volumes 100A-F) published in 2012. This paper describes the methodology and stringent criteria used in the creation of a comprehensive database on tumors noted in animals and humans for the carcinogens reviewed in Volume 100, and for additional Group 1 agents that were identified in subsequent Monographs through Volume 109. The development of this database involved the systematic collection of relevant data on tumors detected in humans and experimental animals identified by the Working Groups that conducted evaluations reported in the IARC Monographs. The database includes all human tumor sites identified by the Working Groups, along with all tumor sites for which there was sufficient evidence in experimental animals. This database provides a basis for assessing the degree of concordance between tumor sites observed in humans and experimental animals for Group 1 agents identified through Volume 109.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".