Sustained increases in skin blood flow are not a prerequisite to initiate sweating during passive heat exposure
Bibliographic record
Abstract
Some studies have observed a functional relationship between sweating and skin blood flow. However, the implications of this relationship during physiologically relevant conditions remain unclear. We manipulated sudomotor activity through changes in sweating efficiency to determine if parallel changes in vasomotor activity are observed. Eight young men completed two trials at 36°C and two trials at 42°C. During these trials, air temperature remained constant while ambient vapor pressure increased from 1.6 to 5.6 kPa over 2 h. Forced airflow across the skin was used to create conditions of high (HiSeff) or low (LoSeff) sweating efficiency. Local sweat rate (LSR), local skin blood flow (SkBF), as well as mean skin and esophageal temperatures were measured continuously. It took longer for LSR to increase during HiSeff at 36°C (HiSeff: 99 ± 11 vs. LoSeff: 77 ± 11 min, P < 0.01) and 42°C (HiSeff: 72 ± 16 vs. LoSeff: 51 ± 15 min, P < 0.01). In general, an increase in LSR preceded the increase in SkBF when expressed as ambient vapor pressure and time for all conditions ( P < 0.05). However, both responses were activated at a similar change in mean body temperature (average across all trials, LSR: 0.26 ± 0.15 vs. SkBF: 0.30 ± 0.18°C, P = 0.26). These results demonstrate that altering the point at which LSR is initiated during heat exposure is paralleled by similar shifts for the increase in SkBF. However, local sweat production occurs before an increase in SkBF, suggesting that SkBF is not necessarily a prerequisite for sweating.
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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".