Ice Cooling Vest on Tolerance for Exercise under Uncompensable Heat Stress
Bibliographic record
Abstract
This study was conducted to evaluate the effectiveness of a commercial, personal ice cooling vest on tolerance for exercise in hot (35°C), wet (65% relative humidity) conditions with a nuclear biological chemical suit (NBC). On three separate occasions, 10 male volunteers walked on a treadmill at 3 miles per hour and 2% incline while (a) seminude (denoted CON), (b) dressed with a nuclear, biological, chemical (NBC) suit with an ice vest (V) worn under the suit (denoted NBCwV); or (c) dressed with an NBC suit but without an ice vest (V) (denoted NBCwoV). Participants exercised for 120 min or until volitional fatigue, or esophageal temperature reached 39.5°C. Esophageal temperature (T(es)), heart rate (HR), thermal sensation, and ratings of perceived exertion were measured. Exercise time was significantly greater in CON compared with both NBCwoV and NBCwV (p < 0.05), whereas T(es), thermal sensation, heart rate, and rate of perceived exertion were lower (p < 0.05). Wearing the ice vest increased exercise time (NBCwoV, 103.6 ± 7.0 min; NBCwV, 115.9 ± 4.1 min) and reduced the level of thermal strain, as evidenced by a lower T(es) at end-exercise (NBCwoV, 39.03 ± 0.13°C; NBCwV, 38.74 ± 0.13°C) and reduced thermal sensation (NBCwoV, 6.4 ± 0.4; NBCwV, 4.8 ± 0.6). This was paralleled by a decrease in rate of perceived exertion (NBCwoV, 14.7 ± 1.6; NBCwV, 12.4 ± 1.6) (p < 0.05) and heat rate (NBCwoV, 169 ± 6; NBCwV, 159 ± 7) (p < 0.05). We show that a commercially available cooling vest can significantly reduce the level of thermal strain during work performed in hot environments.
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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 | 0.001 |
| 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".