The Influence of Activewear Worn Under Standard Work Coveralls on Whole-Body Heat Loss
Bibliographic record
Abstract
This study evaluated the influence of activewear undergarments worn under the standard mining coveralls on whole-body heat exchange and change in body heat content during work in the heat. Each participant performed 60 min of cycling at a constant rate of heat production of 400 W followed by 60 min of recovery in a whole-body calorimeter regulated at 40°C and 15% relative humidity donning one of the four clothing ensembles: (1) cotton underwear and shorts only (Control, CON); (2) Activewear only (ACT); (3) Coveralls+Cotton undergarments (COV+COT); or (4) Coveralls+Activewear undergarments (COV+ACT). In the latter two conditions a hard hat with earmuffs, gloves, and socks with closed toe shoes were worn. We observed that both COV+COT and COV+ACT resulted in a similar mean (±SE) change in body heat content, which was significantly greater compared with the CON and ACT during exercise, suggesting that the rate of thermal strain was elevated to a similar degree in both coverall conditions (CON: 245±32 kJ; ACT: 260±29 kJ; COV+COT: 428±36 kJ; COV+ACT: 466±15 kJ; p<0.001). During recovery, the negative change in body heat content was greater for both COV+COT and COV+ACT compared with the CON and ACT but similar between COV+COT and COV+ACT due to the greater amount of heat stored during exercise (CON: -83±16 kJ; ACT: -104±33 kJ; COV+COT: -198±30 kJ; COV+ACT: -145±12 kJ; p=0.048). Core temperatures and heart rate were also significantly elevated for the COV+COT and COV+ACT compared with the CON and ACT conditions during and following exercise (p<0.05). These results suggest that while activewear undergarments are not detrimental, they provide no thermoregulatory benefit when replacing the cotton undergarment worn under the standard coverall during work 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".