Whole‐body sweat production during exercise: Evaporative heat loss requirement or percent VO <sub>2max</sub> ?
Bibliographic record
Abstract
Under conditions which permit full evaporation of the sweat produced, the evaporative heat loss requirement ( E req ) should determine whole‐body sweat production during exercise. Yet, most exercise temperature regulation studies employ a fixed percentage of maximum oxygen consumption (VO 2max ) when examining sweat production between independent groups. We therefore examined the relationship between sweat production and E req , as well as %VO 2max during exercise. Twenty‐three males either performed exercise at different rates of metabolic heat production (200, 350, 500 W) at a fixed air temperature (30°C), or exercised at a fixed rate of metabolic heat production (290 W) at different air temperatures (30°C, 35°C, 40°C). Whole‐body sudomotor activity (i.e. evaporative heat loss, E sk ) was measured directly by calorimetry. Stepwise multiple regression analysis revealed that end‐exercise values of E req significantly correlated with the variability in end‐exercise values of E sk (p<0.001), while %VO 2max did not (p=0.690). Independently, end‐exercise E req explained ~95% of the variance in end‐exercise E sk values (adjusted R 2 =0.945). These data provide clear evidence that steady‐state sweat production during exercise is determined by E req , not percent of VO 2max . Supported by NSERC grant RGPIN‐298159–2009 (held by GP Kenny).
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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) | 0.002 | 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".