Evaluation\nof Firefighter Exposure to Wood Smoke during\nTraining Exercises at Burn Houses
Bibliographic record
Abstract
Smoke from wood-fueled fires is one\nof the most common hazards\nencountered by firefighters worldwide. Wood smoke is complex in nature\nand contains numerous compounds, including methoxyphenols (MPs) and\npolycyclic aromatic hydrocarbons (PAHs), some of which are carcinogenic.\nChronic exposure to wood smoke can lead to adverse health outcomes,\nincluding respiratory infections, impaired lung function, cardiac\ninfarctions, and cancers. At training exercises held in burn houses\nat four fire departments across Ontario, air samples, skin wipes,\nand urine specimens from a cohort of firefighters (<i>n</i> = 28) were collected prior to and after exposure. Wood was the primary\nfuel used in these training exercises. Air samples showed that MP\nconcentrations were on average 5-fold greater than those of PAHs.\nSkin wipe samples acquired from multiple body sites of firefighters\nindicated whole-body smoke exposure. A suite of MPs (methyl-, ethyl-,\nand propylsyringol) and deconjugated PAH metabolites (hydroxynaphthalene,\nhydroxyfluorene, hydroxyphenanthrene, and their isomers) were found\nto be sensitive markers of smoke exposure in urine. Creatinine-normalized\nlevels of these markers were significantly elevated (<i>p</i> < 0.05) in 24 h postexposure urine despite large between-subject\nvariations that were dependent on the specific operational roles of\nfirefighters while using personal protective equipment. This work\noffers deeper insight into potential health risk from smoke exposure\nthat is needed for translation of better mitigation policies, including\nimproved equipment to reduce direct skin absorption and standardized\nhygiene practices implemented at different regional fire services.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.453 | 0.022 |
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; both teacher heads agree on what is shown here.
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".