Diesel engine exhaust exposures in two underground mines
Bibliographic record
Abstract
Exposure to diesel engine exhaust (DE) is a major concern in underground mines. It has been linked to cardiopulmonary diseases and is classified as a human carcinogen. The goal of this study is to assess DE exposures in workers at two underground gold mines, to compare exposure levels within and between the mines, and to compare different methods of measuring DE exposures, namely respirable combustible dust (RCD), elemental carbon (EC) and total carbon (TC). Ambient and personal breathing zone (PBZ) measurements were taken. Side-by-side monitoring of RCD and of the respirable fraction of EC and TC (ECR and TCR) was carried out in the workers’ breathing zone during full-shift work. Regarding ambient measurements, in addition to ECR, TCR and RCD, a submicron aerosol fraction (less than 1 µm) of EC and TC was also sampled (EC1 and TC1). Average ambient results of 240 µg/m3 in RCD, 150 µg/m3 in ECR and 210 µg/m3 in TCR are obtained. Average PBZ results of 190 µg/m3 in RCD, 84 µg/m3 in ECR and 150 µg/m3 in TCR are obtained. Very good correlation is found between ECR and EC1 with a Pearson correlation coefficient of 0.99 (p < 0.01) calculated between the two log-transformed concentrations. No differences are reported between ECR and EC1, nor between TCR and TC1, since ratios are equal to 1.04, close to 1, in both cases. Highest exposures are reported for load-haul-dump (LHD) and jumbo drill operators and conventional miners. Significant exposure differences are reported between mines for truck and LHD operators (p < 0.01). The average TCR/ECR ratio is 1.6 for PBZ results, and 1.3 for ambient results. The variability observed in the TCR/ECR ratio shows that interferences from non-diesel related organic carbon can skew the interpretation of results when relying only on TC data.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".