CARcinogen EXposure: CAREX
Bibliographic record
Abstract
Cancer is the second common cause of death worldwide and a significant ratio of all cancers is related to occupational and living environments. On the other side, cancer prevalence could be controlled and prevented via policies to improve occupational and living environments. However, a main challenge in prevention of occupational cancer is the lack of knowledge about the exposure rate and number of exposed persons. CAREX database, which is established by the program of Europe against cancer, provides information for the number of exposed persons based on country, carcinogen, and type of industry. CAREX is established in early years of 1990 decade by Finland Institute of Health (FIOH) in cooperation with IARC and European experts, as a tool for estimation of the burden due to occupational cancer in Europe, and shortly thereafter is expanded for use in almost 15 countries in European Union by 55 industrial groups. Several other countries have used CAREX for their countries and have provided some main progressions for the performance model. CAREX project in Canada was modeled in 2007, in an effort to develop a Canadian specific and advanced tool for assessing exposure to carcinogenic agents based on EU CAREX. In this model, not only occupational exposure, but also environmental exposure has been considered. Estimation of exposure with CAREX helps to inform primary prevention activities and to improve global occupational cancer, and its strength points are systematic nature, good coverage and ease of use, and can be used in other countries of the world.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".