Perceptions of Exposure and Mask Use in Wildland Firefighters
Bibliographic record
Abstract
Wildland firefighters are exposed to airborne particulates, polycyclic aromatic hydrocarbons (PAHs), and other hazardous substances. Respiratory protection is indicated, but information is lacking on the tasks and conditions for which mask wearing should be advised. Studies to assess respiratory protection in wildland firefighters were carried out in western Canada in 2021 and 2023. Sampling pumps measured airborne exposures and urinary 1-hydroxypyrene (1-HP) was assayed to indicate PAH absorption. Participants in 2021 reported the time for which they wore the mask during each task. In 2023, the use of masks was reported, and firefighters rated the smoke intensity. In 2021, 72 firefighters were monitored over 164 shifts and, in 2023, 89 firefighters were monitored for 263 shifts. In 2021, mask wearing was highest for those engaged in initial attack and hot spotting. Urinary 1-HP at the end of rotation was highest for those reporting initial attack, working on a prescribed fire and mop-up. In 2023, firefighter ratings of smoke intensity were strongly associated with measured particulate mass and with urinary 1-HP, but masks were not worn more often when there was higher smoke intensity. The data from the literature did not provide a clear indication of high-exposure tasks. Better task/exposure information is needed for firefighters to make informed decisions about mask wearing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".