Active Versus Passive Cooling During Work in Warm Environments While Wearing Firefighting Protective Clothing
Bibliographic record
Abstract
This study examined whether active or passive cooling during intermittent work reduced the heat strain associated with wearing firefighting protective clothing (FPC) and self-contained breathing apparatus (SCBA) in the heat (35 degrees Celsius, 50% relative humidity). Fifteen male Toronto firefighters participated in the heat-stress trials. Subjects walked at 4.5 km.h(-1) with 0% elevation on an intermittent work (50 min) and rest (30 min) schedule. Work continued until rectal temperature (T(re)) reached 39.5 degrees Celsius, or heart rate (HR) reached 95% of maximum or exhaustion. One of three cooling strategies, forearm submersion (FS), mister (M), and passive cooling (PC) were employed during the rest phases. Tolerance time (TT) and total work time (WT) (min) were significantly increased during FS (178.7 +/- 13.0 and 124.7 +/- 7.94, respectively) and M (139.1 +/- 8.28 and 95.1 +/- 4.96, respectively), compared with PC (108.0 +/- 3.59 and 78.0 +/- 3.59). Furthermore, TT and WT were significantly greater in FS compared with M. Rates of T(re) increase, HR and T-(sk) were significantly lower during active compared with passive cooling. In addition, HR and T(re) values in FS were significantly lower compared with M after the first rest phase. During the first rest phase, T(re) dropped significantly during FS (approximately 0.4 degree Celsius) compared with M (approximately 0.08 degree Celsius) while PC increased (approximately 0.2 degree Celsius). By the end of the second rest period T(re) was 0.9 degree Celsius lower in FS compared with M. The current findings suggest that there is a definite advantage when utilizing forearm submersion compared with other methods of active or passive cooling while wearing FPC and SCBA in the heat.
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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) | 0.001 | 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".