Characterization of Noise and Carbon Monoxide Exposures among Professional Firefighters in British Columbia
Bibliographic record
Abstract
OBJECTIVES: To characterize exposures to noise and carbon monoxide (CO) among firefighters in British Columbia, Canada. METHODS: Subjects were recruited from 13 fire halls across three municipalities in Metro Vancouver. Personal full-shift noise and CO samples were collected using datalogging noise dosimeters and CO monitors on both day and night shifts. Determinants of exposure (DoE) information were recorded by trained research staff and hygienists through direct observation during the measurement period. RESULTS: In total, 113 noise and 156 CO samples were collected from 45 male firefighters, aged 41.0 ± 7.2 years with 14.2 ± 9.0 years of experience. Mean L(eq) and peak noise levels were 81.1 ± 4.8 dBA and 137.1 ± 5.2 dB, respectively; 45% of samples exceeded occupational limits. Noise levels were significantly greater on day shifts, among firefighters in non-supervisory jobs, for those working on engine and rescue trucks, by number of emergency calls they attended and in particular for motor vehicle accident (MVA) and building alarms calls, if subjects worked near or used fire equipment, or if they participated in active firefighting training activities. Full-shift and peak CO levels were 1.0 ppm [geometric mean (GM) = 0.7, geometric standard deviation (GSD) = 1.8] and 42.9 ppm (GM = 9.95, GSD = 5.6), respectively; 1% of CO samples exceeded occupational limits. Both full-shift and peak CO levels were significantly correlated by number of MVAs and building alarms calls. CONCLUSIONS: Our results show that firefighters may be at an increased risk of exposure to high noise levels, but CO exposures were lower than anticipated. Additional exposure studies are needed to confirm our results and to better understand the DoE to noise and CO among this occupational group.
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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".