Work Rate during Self-paced Exercise is not Mediated by the Rate of Heat Storage
Bibliographic record
Abstract
PURPOSE: To date, there have been mixed findings on whether greater anticipatory reductions in self-paced exercise intensity in the heat are mediated by early differences in rate of body heat storage. The disparity may be due to an inability to accurately measure minute-to-minute changes in whole-body heat loss. Thus, we evaluated whether early differences in rate of heat storage can mediate exercise intensity during self-paced cycling at a fixed rate of perceived exertion (RPE of 16; hard-to-very-hard work effort) in COOL (15°C), NORMAL (25°C), and HOT (35°C) ambient conditions. METHODS: On separate days, nine endurance-trained cyclists exercised in COOL, NORMAL, and HOT conditions at a fixed RPE until work rate (measured after first 5 min of exercise) decreased to 70% of starting values. Whole-body heat loss and metabolic heat production were measured by direct and indirect calorimetry, respectively. RESULTS: Total exercise time was shorter in HOT (57 ± 20 min) relative to both NORMAL (72 ± 23 min, P = 0.004) and COOL (70 ± 26 min, P = 0.045). Starting work rate was lower in HOT (153 ± 31 W) compared with NORMAL (166 ± 27 W, P = 0.024) and COOL (170 ± 33 W, P = 0.037). Rate of heat storage was similar between conditions during the first 4 min of exercise (all P > 0.05). Thereafter, rate of heat storage was lower in HOT relative to NORMAL and COOL until 30 min of exercise (last common time-point between conditions; all P < 0.05). Further, rate of heat storage was significantly higher in COOL compared with NORMAL at 15 min (P = 0.026) and 20 min (P = 0.020) of exercise. No differences were measured at end exercise. CONCLUSIONS: We show that rate of heat storage does not mediate exercise intensity during self-paced exercise at a fixed RPE in cool to hot ambient conditions.
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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".