The Influence of Aerobic Capacity on Human Thermoregulation in Individuals Matched For Body Morphology
Bibliographic record
Abstract
Six fit (F) (VO2max: 60.5±4.8 ml/kg/min) and six unfit (UF) (VO2max: 40.1±3.0 ml/kg/min) males matched for body mass (F: 78.7±6.3 kg; UF: 78.9±7.9 kg) and surface area (F: 1.98±0.09 m2; UF: 1.95±0.09 m2) cycled for 60-min at 1) 105 W external workload; and 2) 60% of VO2max. Rectal temperature (Tre), metabolic heat production (Hprod), heart rate (HR) and perceived exertion (RPE) was measured throughout. Whole-body sweat loss (WBSL) was measured using body mass changes and forehead sweat rate (LSRhead) with technical absorbents over the last 5-min. At a 105 W workload, Hprod was similar (F:544±41 W; UF:531±44 W, P=0.46) as was the change in Tre from rest to end-exercise (F:0.94±0.18°C; UF:0.86±0.19°C, P=0.48), and WBSL (F:430±87 mL; UF:441±45 mL, P=0.79). However, LSRhead was greater (P=0.014) in UF (1.33±0.43 ml/cm2/min) versus F (0.67±0.32 ml/cm2/min) as well as HR (P=0.048) and RPE (P=0.013). At 60% VO2max, F had greater values than UF for Hprod (F:848±69 W; UF:597±98 W, P<0.001), change in Tre (F:1.44±0.28°C; UF:0.91±0.27°C, P=0.001), WBSL (F:837±161 mL; UF:491±63 mL, P=0.001), LSRhead (F:2.68±1.12 ml/cm2/min; UF:1.44±0.64 ml/cm2/min, P=0.047), but not HR (P=0.73) or RPE (P=0.45). Changes in Tre and WBSL are determined by Hprod and body mass, not VO2max. A greater LSRhead in UF at the same Hprod is attributed to greater sympathetic drive evidenced by greater HR and RPE. Support: NSERC Discovery grant (Holder: O. Jay)
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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) | 0.001 | 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".