Influence of fitness on core temperature thresholds for exercise ventilation (882.6)
Bibliographic record
Abstract
During incremental exercise from rest to exhaustion, core temperature thresholds are evident at exercise intensities greater than that at the anaerobic threshold or the onset of blood lactate accumulation. The effect of aerobic fitness on these core temperature thresholds remains poorly understood. PURPOSE: To compare oesophageal temperature (T ES ) thresholds for exercise ventilation (V E ) between competitive, trained and untrained males during incremental exercise. HYPOTHESIS: Trained athletes will demonstrate a greater T ES threshold for V E than untrained males. Methods: Seven college‐aged competitive male cyclists with a VO 2MAX of 63.9 ± 3.7 mL•min ‐1 •kg ‐1 (mean ± SD) and 7 college‐aged untrained males with a VO 2MAX of 53.9 ± 3.5 mL•min ‐1 •kg ‐1 volunteered for the study that approved by the SFU Office of Research Ethics. V E and its components were measured with a breath‐by‐breath metabolic cart and volunteers pedaled an electrically braked cycle ergometer at an ambient temperature of ~22 o C. Each incremental exercise test started with a load of 60 W on the cycle ergometer that was incremented by 35 W/min until exhaustion. Core temperature thresholds from plots of ventilatory equivalents for oxygen (V E /VO 2 ) and carbon dioxide (V E /VCO 2 ) as a function of T ES were 1.3 ° C greater (P<0.01) in the trained vs. the untrained group. The trained group relative to the untrained cyclists had a higher (P<0.01) anaerobic threshold as determined from plots of V E /VO 2 and V E /VO 2 as a function of VO 2 . CONCLUSION: Evidence supports the hypothesis that increased aerobic fitness results in a higher core temperature threshold for V E during incremental exercise. Grant Funding Source : Supported by NSERC and CFI
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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".