Diesel exhaust exposures in an underground mine
Bibliographic record
Abstract
The mining industry is a major contributor to the Quebec and Canadian economy.In Canada, more than 400,000 workers are involved directly or indirectly in the mining industry.Health and safety challenges in underground mines are unique due to the complexity of the environment.Exposure to diesel engine exhaust is a major concern in underground mines due to the presence of off-road diesel-powered machinery.Diesel engine exhaust has been linked to cardiopulmonary diseases and was classified as a human carcinogen by the International Agency for Research on Cancer in 2012.Here we present the results of a preliminary study conducted in an underground gold mine in the province of Quebec in 2014-15 to assess diesel engine exhaust exposures among mine workers.The goal of this study was 1) to compare three surrogates of diesel engine exhaust exposure (total carbon, elemental carbon and respirable combustible dust) and 2) to assess diesel exhaust concentrations among the similar exposure groups and the variability of the exposures.Results were also compared to the Ontario and Quebec occupational exposure limits for compliance purposes.Environmental and breathing zone measures were taken.Average environmental results of 0.31 mg/m 3 in total carbon, 0.24 mg/m 3 in elemental carbon, and 0.17 mg/m 3 in respirable combustible dust were obtained.Average breathing zone results of 0.32 mg/m 3 in total carbon, 0.19 mg/m 3 in elemental carbon and 0.36 mg/m 3 in respirable combustible dust were obtained.The highest exposures were obtained in the conventional, scooptram and jumbo workers.The average total carbon/elemental carbon ratio was 1.3 for environmental measures, and 1.9 for breathing zone measures.The variability observed in the total carbon/elemental carbon ratio shows that interferences from nondiesel related organic carbon can skew the interpretation of results when relying only on total carbon data.However, more data is needed to support this.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".