Rate of heat storage does not influence exercise intensity at a fixed rating of perceived exertion
Bibliographic record
Abstract
This study investigated whether different rates of decline in self‐selected work rate ( W ) were mediated by early differences in heat storage. Five males cycled at a W to maintain a perceived exertion of 16 (Borg scale) at 35°C and 25°C. Heat production ( H prod ) was estimated via indirect calorimetry. Esophageal (T es ) and skin (T sk ) temperatures, as well as local sweat rates on the lower back (LSR back ) and thigh (LSR thigh ), were measured throughout. The level of muscle activation in vastus lateralis (VL) was assessed via integrated surface electromyography (iEMG). While dry heat loss was lower at 35°C (P<0.01), sweating responses were elevated, indicated by greater LSR thigh at 2 min (P<0.05), a trend toward greater LSR back (P=0.07), and earlier onsets at both sites (P=0.02). Similar H prod and rates of change in T es were found in both conditions (P>0.05), yet at 35°C, exercise time was shorter (35°C: 22.6±4.9 min; 25°C: 32.1±10.4 min; P=0.03), and W (% of initial W ) declined more rapidly by 15 min (35°C: 89.2±8.2%; 25°C: 82.2±8.0%; P<0.01). Mean T sk was lower throughout exercise at 25°C (P<0.01). Similar changes in VL iEMG activity were found at 25°C and 35°C (P=0.28). In conclusion, reductions in W occur without early differences in heat storage between air temperatures, implicating factors other than the rate of heat storage, such as T sk , in the regulation of exercise intensity. Supported by a NSERC Discovery Grant (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".