Development of Quantitative Estimates of Wood Dust Exposure in a Canadian General Population Job-Exposure Matrix Based on Past Expert Assessments
Bibliographic record
Abstract
Objectives: The CANJEM general population job-exposure matrix summarizes expert evaluations of 31 673 jobs from four population-based case-control studies of cancer conducted in Montreal, Canada. Intensity in each CANJEM cell is represented as relative distributions of the ordinal (low, medium, high) ratings of jobs assigned by the experts. We aimed to apply quantitative concentrations to CANJEM cells using Canadian historical measurements from the Canadian Workplace Exposure Database (CWED), taking exposure to wood dust as an example. Methods: We selected 5170 personal and area wood dust measurements from 31 occupations (2011 Canadian National Occupational Classification) with a non-zero exposure probability in CANJEM between 1930 and 2005. The measurements were taken between 1981 and 2003 (median 1989). A Bayesian hierarchical model was applied to the wood dust concentrations with occupations as random effects, and sampling duration, year, sample type (area or personal), province, and the relative proportion of jobs exposed at medium and high intensity in CANJEM cells as fixed effects. Results: The estimated geometric mean (GM) concentrations for a CANJEM cell with all jobs exposed at medium or high intensity were respectively 1.3 and 2.4 times higher relative to a cell with all jobs at low intensity. An overall trend of -3%/year in exposure was observed. Applying the model estimates to all 198 cells in CANJEM with some exposure assigned by the experts, the predicted 8-hour, personal wood dust GM concentrations by occupation for 1989 ranged from 0.48 to 1.96 mg m-3. Conclusions: The model provided estimates of wood dust concentrations for any CANJEM cell with exposure, applicable for quantitative risk assessment at the population level. This framework can be implemented for other agents represented in both CANJEM and CWED.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".