Evaporative Heat Loss and Changes in Core Temperature Set‐Point during Different Absolute Workloads
Bibliographic record
Abstract
Previous work suggests changes in core temperature set‐point during exercise are determined by relative workload (i.e. % of VO 2max ). Concurrent rates of metabolic heat production (M‐W), whole‐body evaporative (EHL) and dry heat loss (DHL) were measured by whole‐body direct calorimetry in 5 males throughout 90‐min of cycling at 3 absolute oxygen uptakes of 1.82±0.05 (hi), 1.26±0.05 (med) and 0.65±0.03 (low) L‐min’ at 30°C and 30% RH. Due to the variation in VO 2max between participants (3.24 to 4.44 L·min −1 ) these workloads corresponded to a range of relative workloads within each condition. Rectal temperature (T re ) was also measured throughout. Sustained levels of M‐W obtained throughout exercise were 500±17 (hi), 348±10 (med) and 203±10 W (low). After 90‐min, T re was steady at 0.96±0.13, 0.70±0.10 and 0.23±0.03°C above rest for hi, med and low respectively. EHL was 423±18, 290±10 and 150±16 W respectively and DHL was 41±10, 45±15 and 58±14 W. Within condition, EHL varied little between participants despite relative workloads differing by 15–20%. End‐exercise EHL appears to be determined by absolute metabolic workload rather than relative workload. Variations in core temperature set‐point within condition are attributed to individual differences in sweating sensitivity during the early stages of exercise; and effects of body mass and composition upon the ‘passive’ system. Supported by NSERC Grant.
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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".