Historical exposure to wood dust in Ontario, Canada
Bibliographic record
Abstract
Objectives Sinonasal cancer due to wood dust exposure is well-recognised as an occupational cancer in Europe, but not in North America, possibly due to differences in tree species or exposure levels. As the first step in developing an exposure matrix for an epidemiologic study, we characterised occupational exposure to wood dust in Ontario. Methods Exposure data was obtained from Ontario (collected for surveillance and compliance purposes). Industry and occupation codes were recorded for linkage with population data. Simple descriptive analyses of wood dust exposure by industry and occupation were prepared. Results There were 3734 wood dust samples available (1780 hardwood, 1430 softwood, 524 not classified). Interestingly, 73% were above the current threshold limit value of 1.0 mg/m3 and 26% were above the historical TLV of 5 mg/m3. After removing outliers, the mean concentration of hardwood dust samples was 5.6 mg/m3 (GM: 2.4 mg/m3) and for softwoods was 3.9 mg/m3 (GM: 1.8 mg/m3). A slight downward trend in mean concentration over time was observed. Most samples (2975) were taken in the wood products and furniture manufacturing industries. Conclusions Historical wood dust exposure levels in Ontario were high, with a large proportion of measurements above the exposure limits. These data indicate that hardwood dust levels have been as high in Canada as they were in Europe in some industries and occupations. The development of this JEM for Canadian exposure to wood dust is a key step in determining whether differences from the European situation exist via a new epidemiologic study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".