Estimating Historical Exposure to Respirable Crystalline Silica in the Mining Industry in Ontario, Canada Using a Newly Developed Exposure Database
Bibliographic record
Abstract
OBJECTIVES: To use the recently developed Ontario Mining Exposure Database (OMED) to describe historical silica exposure in the Ontario metal mining industry and identify predictors of historical silica exposure. METHODS: Personal respirable crystalline silica (RCS) data for metal mines were extracted from OMED and included both individual and summary measures, where multiple exposure measurements (n > 1) were aggregated and entered as a single exposure value (n = 1). Data were stratified by sample location (underground/surface) for analysis. Monte Carlo simulation was used to simulate individual measures from the summary measures. A fixed effects multiple linear regression model was used to assess the effects of commodity (ore mined), sample year, source of exposure data, and occupational group on RCS concentration. Parameter estimates (β), standard errors, and 95% upper and lower confidence intervals were reported. RESULTS: The OMED contained 12 995 silica measurements. After limiting to RCS measurements in metal mines, and measures with sufficient information for analysis, 2883 RCS measurements collected from 1974 to 1991 remained, including 2816 individual and 67 summary measurements. In total, 321 individual RCS measurements were simulated from the 67 summary measures. The analysis database contained 2771 (12% simulated) underground measurements and 366 surface measurements (0% simulated). In the underground group, an overall geometric mean (GM) of 0.05 [geometric standard deviation (GSD) 3.09] mg m-3 was estimated with a 6% annual decrease over time. In this group, the commodity with the highest average RCS level was zinc mines (GM = 0.07 mg m-3) and the lowest was iron mines (GM = 0.01 mg m-3). In the surface group, an overall GM of 0.05 (GSD 3.70) mg m-3 was estimated with an 8% decreased over time. In this group, the commodity with the highest average RCS level was gold mines (GM = 0.07 mg m-3) and the lowest was zinc mines (GM = 0.03 mg m-3). In both groups, company collected data had lower estimated RCS compared with regulator collected data. CONCLUSIONS: Historical RCS levels decreased over time. Mean measurements exceeded the American Conference of Governmental Industrial Hygienists current health-based threshold limit value (0.025 mg m-3). The main predictors of exposure were commodity, source of exposure data, and sample year. However, low R2 and high GSD values suggest additional predictors of RCS exposures in Ontario's metal mines exist that were unavailable in OMED.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".