Determinants of Exposure to Metalworking Fluid Aerosol in Small Machine Shops
Bibliographic record
Abstract
The purpose of this study was to evaluate personal exposure to metalworking fluid (MWF) aerosols in very small machine shops (1-8 machinists per shop) and to investigate workplace factors associated with exposures. A total of 20 willing machine shops in Vancouver, Canada (from 46 eligible shops, 43%) and 88 machinists participated (participation rate for machinists 92%). Most machinists wore two personal sampling trains (an open-faced 37 mm cassette and a PM10 impactor) on each of two full work shifts. Observational data were collected regarding potential determinants of exposure at 15 min intervals throughout each shift. A total of 322 personal samples were taken over 54 days. Mean aerosol exposure was 0.32 mg/m3 (range 0.06-2.19) for the 37 mm cassette samples and 0.27 mg/m3 (range 0.026-3.67) for PM10. Exposures from the two sampler types were highly correlated (R = 0.86). The mean shop-specific ratio comparing exposure from the 37 mm cassette with that from the PM10 sampler was 1.43 and varied significantly across shops, ranging from 0.97 to 2.19. Machine, task and shop characteristics associated with significantly increased aerosol exposure included the proportion of time spent grinding, operating an enclosed computer controlled machine, the presence of welding in the shop for both sampler types and the number of machines using MWF for PM10 samples only. Factors associated with reduced aerosol exposure included machining aluminum, milling, the height (and shape) of the shop roof (for both sample types) and the presence of mechanical shop ventilation (for the 37 mm cassette samples).
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".