Local versus whole-body sweating adaptations following 14 days of traditional heat acclimation
Bibliographic record
Abstract
The purpose of this study was to examine if local changes in sweat rate following 14 days of heat acclimation reflect those that occur at the whole-body level. Both prior to and following a 14-day traditional heat acclimation protocol, 10 males exercised in the heat (35 °C, ∼20% relative humidity) at increasing rates of heat production equal to 300 (Ex1), 350 (Ex2), and 400 (Ex3) W·m(-2). A 10-min recovery period followed Ex1, while a 20-min recovery period separated Ex2 and Ex3. The exercise protocol was performed in a direct calorimeter to measure whole-body sweat rate and, on a separate day, in a thermal chamber to measure local sweat rate (LSR), sweat gland activation (SGA), and sweat gland output (SGO) on the upper back, chest, and mid-anterior forearm. Post-acclimation, whole-body sweat rate was greater during each exercise bout (Ex1: 14.3 ± 0.9; Ex2: 17.3 ± 1.2; Ex3: 19.4 ± 1.3 g·min(-1), all p ≤ 0.05) relative to pre-acclimation (Ex1: 13.1 ± 0.6; Ex2: 15.4 ± 0.8; Ex3: 16.5 ± 1.3 g·min(-1)). In contrast, only LSR on the forearm increased with acclimation, and this increase was only observed during Ex2 (Post: 1.32 ± 0.33 vs. Pre: 1.06 ± 0.22 mg·min(-1)·cm(-2), p = 0.03) and Ex3 (Post: 1.47 ± 0.41 vs. Pre: 1.17 ± 0.23 mg·min(-1)·cm(-2), p = 0.05). The greater forearm LSR post-acclimation was due to an increase in SGO, as no changes in SGA were observed. Overall, these data demonstrate marked regional variability in the effect of heat acclimation on LSR, such that not all local measurements of sweat rate reflect the improvements observed at the whole-body level.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".