Recent trends in respirable crystalline silica and elemental carbon exposure in the Ontario, Canada mining industry
Bibliographic record
Abstract
INTRODUCTION: Mining is a high-hazard industry with significant occupational disease risks. Despite this there is limited data describing current exposure conditions. The aim of this short communication is to share recent exposure data from underground mines in Ontario, Canada. METHODS: Data from underground mines were accessed through a freedom of information request. Data were cleaned and standardized. Data contained measurements of several hazards from 2013 to 2018; analysis focused on personal samples for respirable crystalline silica (RCS) and elemental carbon (EC) from 2014 to 2018. Descriptive statistics were calculated overall and by sampling year; comparisons were made to current occupational exposure limits. Linear regression models were constructed to examine time trends. RESULTS: EC exposures decreased significantly, ~10% per year over the measurement period (2014 to 2018). Overall 14% of EC measurements were above the current mining exposure limit (0.12 mg/m3 EC) in Ontario, Canada. Results for silica did not show a statistically significant trend but did suggest a reduction of ~1.8% per year. Almost one-third of the RCS measurements were above the American Conference of Governmental Industrial Hygienists (ACGIH) recommended threshold (0.025 mg/m3). CONCLUSIONS: Current exposure data is needed to understand workers' exposure and support occupational disease prevention. Recent data from the Ontario mining industry suggests that exposure to elemental carbon decreased significantly from 2014 to 2018, but the annual reduction for silica exposure was not nearly as substantial. Mining workers continue to be exposed to levels of EC and RCS that are hazardous to health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".