Does “Waiting to Start” in Extreme Cold Conditions Alter Physiological, Perceptual and Exercise Performance Responses in Males and Females?
Bibliographic record
Abstract
We investigated the influence of different-length interim periods after a standardized warm-up on the physiological, perceptual and performance responses in males and females. Fourteen participants (eight females, six males; age: 24.7 ± 5.6 years; V̇O2max 54.6 ± 5.5 mL/kg/min) completed three environmental chamber trials [0 (CON), 6 (6IP) or 12 (12IP)-minute interim period] preceded by the same 15 min warm-up and followed by a subsequent 8-min running performance trial at −15.0 °C. The maximal knee extension force, heart rate, muscle oxygenation, thermal state, cold discomfort and perceived leg discomfort were measured. The distance run was the same between conditions but the average (p = 0.008) and peak heart rates (p = 0.034), as well as the thermal state (p < 0.001), were all greater in the CON compared to 12IP. Females did have heavier legs and felt colder at the end of the interim periods, with continued heavier legs and cold discomfort across the performance trial, although these increases were not significant (p > 0.05). Thus, increasing the rest time in severe cold alters physiological and perceptual responses, especially in females, but does not influence running performance over 8-min. It is recommended that minimizing wait times will reduce the effects of severe cold air cooling before an outdoor winter sport competition.
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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".