S10-2 Carex canada: innovations and applications
Bibliographic record
Abstract
Ten years ago the CAREX Canada project was initiated with the objective of identifying how many Canadians are exposed to workplace and environmental carcinogens as well as how and where they are exposed. While the occupational component of the project was largely based on the European CAREX project, CAREX Canada sought to incorporate a number of innovations based on the Finnish job exposure matrix (FinJEM), other CAREX projects such as Costa Rica’s, and large exposure database projects that were being initiated at the time. CAREX Canada sought to enhance the original CAREX model in two major ways. First, prevalence of exposure was assessed based on both industry (328 categories) and occupation (520 categories), using much finer groups than previous projects. This allowed for both better assessment of exposure and a finer level of reporting for targeting prevention efforts. Second, where possible, levels of exposure were estimated as three categories of low, medium, and high using cut-points based on occupational exposure limits. To facilitate this classification, the Canadian Workplace Exposure Database was created, using several hundred thousand measurements acquired from regulatory agencies. For example, we estimated that 380,000 workers are exposed to crystalline silica, 14% high (>0.025 mg/m3), 39% moderate (0.0125–0.025 mg/m3), and 47% low. These enhancements have made CAREX Canada a much more effective tool for use in both prevention and, through creation of job exposure matrixes, epidemiologic applications. The greater granularity of the prevalence estimates and the availability of measured exposure levels for many common carcinogens have facilitated the use of this data for cancer surveillance and burden of cancer projects. Level of exposure estimation has been particularly useful, but has been limited to what can be called the “data-rich” exposures. This project has also provided a systematic means of identifying where significant data gaps exist.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.189 | 0.050 |
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".