42 Interventions to reduce PAH exposure in wildland firefighters
Bibliographic record
Abstract
Abstract Wildland firefighters are repeatedly exposed to smoke and particles during increasingly long and fierce wildfire seasons with both inhalation and skin absorption of polycyclic aromatic hydrocarbons (PAHs). Wildfire firefighters do not habitually use respiratory protection and good skin hygiene to reduce exposure through contaminated clothing and equipment can be difficult for those living in camp. We introduced interventions to reduce exposures during the 2019, 2021 and 2023 fire seasons, with urinary 1-hydroxypyrene (1-HP), as the outcome marker for PAH absorption. Secondary outcomes included self-reports of mask wearing and post fire respiratory symptom. Sources of PAH exposure were evaluated by personal sampling pumps and skin wipes. A total of 281 firefighters from the western Canadian provinces of Alberta and British Columbia took part over the three fire seasons. Interventions in 2019 and 2021 addressed both skin hygiene and mask wearing: the 2023 intervention compares the effects of three types of mask, half-face with P100 cartridges fire mask and mesh mask. Data collection for the 2023 season has been completed and analysis is in progress, with results expected early in 2023. Data from previous seasons have shown a strong relation between ambient air and skin wipe PAHs and urinary 1-HP, with lower than predicted values in those randomly allocated to wear a mask. A minority of those allocated masks wore them little or not at all, citing discomfort and difficulties carrying out their tasks. The 2023 season data will give clearer indication of the type of mask most efficient and acceptable.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".