CAREX Canada: an enhanced model for assessing occupational carcinogen exposure
Bibliographic record
Abstract
OBJECTIVES: To estimate the numbers of workers exposed to known and suspected occupational carcinogens in Canada, building on the methods of CARcinogen EXposure (CAREX) projects in the European Union (EU). METHODS: CAREX Canada consists of estimates of the prevalence and level of exposure to occupational carcinogens. CAREX Canada includes occupational agents evaluated by the International Agency for Research on Cancer as known, probable or possible human carcinogens that were present and feasible to assess in Canadian workplaces. A Canadian Workplace Exposure Database was established to identify the potential for exposure in particular industries and occupations, and to create exposure level estimates among priority agents, where possible. CAREX EU data were reviewed for relevance to the Canadian context and the proportion of workers likely to be exposed by industry and occupation in Canada was assigned using expert assessment and agreement by a minimum of two occupational hygienists. These proportions were used to generate prevalence estimates by linkage with the Census of Population for 2006, and these estimates are available by industry, occupation, sex and province. RESULTS: CAREX Canada estimated the number of workers exposed to 44 known, probable and suspected carcinogens. Estimates of levels of exposure were further developed for 18 priority agents. Common exposures included night shift work (1.9 million exposed), solar ultraviolet radiation exposure (1.5 million exposed) and diesel engine exhaust (781 000 exposed). CONCLUSIONS: A substantial proportion of Canadian workers are exposed to known and suspected carcinogens at work.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".