CAREX Canada: an enhanced model for assessing occupational carcinogen exposure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it