Heat strain in different hot environments hiking in wildland firefighting garments
Bibliographic record
Abstract
Wildland firefighters can work at high intensity in hot environments for extended periods of time. The resulting heat strain may be modified by the environmental conditions (i.e., ambient temperature and humidity [RH]) even at equal wet-bulb globe temperatures (WBGTs). This investigation assessed if a hot and dry condition would create greater strain than moderate and high humidity at equivalent WBGT (28 °C). Twelve participants (age 24 ± 2 year) walked at 40%–50% maximum aerobic capacity for 90 and 40 min separated by a 20 min rest in dry (40 °C, 20% RH), moderate-humidity (34 °C, 50% RH), and high-humidity (29 °C, 90% RH) conditions wearing fire-resistant jacket, pants, gloves, and helmet with the neck and face exposed. Peak core temperature was higher in moderate-humidity (38.9 ± 0.2 °C, p = 0.01) and high-humidity (38.9 ± 0.6 °C, p < 0.01) than dry condition (38.5 ± 0.3 °C). Average net heat gain was less in dry (33 ± 22 W) compared to moderate-humidity (38 ± 23 W, p < 0.01) and high-humidity (39 ± 28 W, p < 0.01). Peak heart rate (174 ± 14 bpm, p = 0.94), physiological strain index (7.7 ± 1.4 score, p = 0.99), perceived exertion (8 ± 2 rating, p = 0.97), and perceptual strain index (7.3 ± 1.6 score, p = 0.99) were not different in high-humidity compared to the dry condition (167 ± 19 bpm, 6.9 ± 1.3 score, 6 ± 2 rating, 7.3 ± 1.7 score, respectively). Whole-body sweat rate (15 ± 6 mL/min, p = 0.58) and thermal sensation (7 ± 1 rating, p = 0.37) were not different. Hiking in a humid condition while wearing protective garments creates greater exertional heat strain compared to a dry condition of equivalent WBGT. Wildland firefighters should consider extra strategies to mitigate hyperthermia when humidity is high.
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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 | 0.000 | 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) | 0.002 | 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".