Evaluation of Firefighter Exposure to Wood Smoke during Training Exercises at Burn Houses
Bibliographic record
Abstract
Smoke from wood-fueled fires is one of the most common hazards encountered by firefighters worldwide. Wood smoke is complex in nature and contains numerous compounds, including methoxyphenols (MPs) and polycyclic aromatic hydrocarbons (PAHs), some of which are carcinogenic. Chronic exposure to wood smoke can lead to adverse health outcomes, including respiratory infections, impaired lung function, cardiac infarctions, and cancers. At training exercises held in burn houses at four fire departments across Ontario, air samples, skin wipes, and urine specimens from a cohort of firefighters (n = 28) were collected prior to and after exposure. Wood was the primary fuel used in these training exercises. Air samples showed that MP concentrations were on average 5-fold greater than those of PAHs. Skin wipe samples acquired from multiple body sites of firefighters indicated whole-body smoke exposure. A suite of MPs (methyl-, ethyl-, and propylsyringol) and deconjugated PAH metabolites (hydroxynaphthalene, hydroxyfluorene, hydroxyphenanthrene, and their isomers) were found to be sensitive markers of smoke exposure in urine. Creatinine-normalized levels of these markers were significantly elevated (p < 0.05) in 24 h postexposure urine despite large between-subject variations that were dependent on the specific operational roles of firefighters while using personal protective equipment. This work offers deeper insight into potential health risk from smoke exposure that is needed for translation of better mitigation policies, including improved equipment to reduce direct skin absorption and standardized hygiene practices implemented at different regional fire services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".