Development of a database on key characteristics of human carcinogens
Bibliographic record
Abstract
A database on mechanistic characteristics of human carcinogenic agents was developed by collecting mechanistic information on agents identified as human carcinogens (Group 1) by the International Agency for Research on Cancer (IARC) in the IARC Monographs on the Evaluation of Carcinogenic Risks to Humans. A two-phase process is described for the construction of the database according to 24 toxicological endpoints, derived from appropriate test systems that were acquired from data obtained from the mechanisms sections of the IARC Monographs (Section 4) and a supplementary PubMed search. These endpoints were then aligned with 10 key characteristics of human carcinogens that reflect the broader attributes of these agents relating to the development of cancer in humans. The considerations involved in linking of toxicological endpoints to key characteristics are described and specific examples of the determination of key characteristics for six specific agents (tamoxifen, hepatitis B virus, arsenic, ultraviolet and solar radiation, tobacco smoking, and dioxin) are provided. Data for humans and animals were tabulated separately, as were results for in-vivo and for in-vitro sources of information. The database was constructed to support a separate analysis of the expression of these endpoints by 86 Group 1 carcinogens, in-vivo and in-vitro along with an analysis of the key characteristics of these agents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".