Rate of Change of Skin Temperature Influences Maximal Pulmonary Ventilation Before and Following Exercise
Bibliographic record
Abstract
Static increases of esophageal (TES) and mean skin (TSK) temperatures have been shown to influence pulmonary ventilation (VE) during passive hyperthermia. It remains unresolved if dynamic (dTSK/dt) skin temperature changes contribute to thermal hyperpnea and if this response is influenced by a build up of exercise metabolites. PURPOSE: It was hypothesized peak pulmonary ventilation responses will increase proportionately to dTSK/dt with stable normothermic core temperatures in both pre- and post-exercise conditions. METHODS: Six male participants (Height: 1.68 ± 0.06 m, Weight: 67.2 ± 11.9 kg and Age 23.2 ± 3.9 yr; mean ± SD) were irradiated with heat lamps for a 10 min period followed by no irradiation for 5 min before and after ∼18 min of exercise at 62.4% VO2PEAK. Analysis was conducted with a repeated measures ANOVA with factors of dTSK/dt (Levels: Positive Rate, Negative Rate) and Exercise State (Levels: Pre- and Post-Exercise). RESULTS: There was no main effect of Exercise State on peak VE (F=2.4, p=0.18), peak VE/VO2 (F=0.8, p=0.42) or peak VE/VCO2 (F=0.8, p=0.40) responses. There was a main effect of dTSK/dt (F=28.8, p<0.001) on peak VE as it increased from 13.2 ± 2.8 L/min at rest to 21.3 ± 4.9 L/min during positive and to 22.0 ± 5.0 L/min during negative changes in dTSK/dt. There were also main effects of dTSK/dt on peakVE/VO2 (F=25.1, p<0.001) and on peak VE/VCO2 (F=36.1, p<0.001) responses. CONCLUSIONS: Dynamic increases and decreases of mean skin temperature give proportional increases in resting peak pulmonary ventilation and this response is not influenced by the muscle chemoreflex. Supported by the Natural Sciences and Engineering Research Council of Canada.
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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".