The effect of cold ambient temperature and preceding active warm-up on lactate kinetics in female cyclists and triathletes
Bibliographic record
Abstract
The aim of this study was to evaluate the effect of cold ambient temperature on lactate kinetics with and without a preceding warm-up in female cyclists/triathletes. Seven female cyclists/triathletes participated in this study. The randomized, crossover study included 3 experimental visits that comprised the following conditions: (i) thermoneutral temperature (20 °C; NEU); (ii) cold temperature (0 °C) with no active warm-up (CNWU); and (iii) cold temperature (0 °C) with 25-min active warm-up (CWU). During each condition, participants performed a lactate threshold (LT) test followed by a time to exhaustion trial at 120% of the participant’s peak power output (PPO) as determined during prior peak oxygen consumption testing. Power output at LT with CNWU was 10.2% ± 2.6% greater than with NEU, and the effect was considered very likely small (effect size (ES) = 0.59, 95%–99% likelihood). Power output at LT with CNWU was 4.2% ± 5.4% greater than with CWU; however, the effect was likely trivial (ES = 0.25, 75%–95% likelihood). At LT, there were no significant differences between interventions groups in oxygen consumption, blood lactate concentration, heart rate, or rating of perceived exertion. Time to exhaustion at 120% at PPO was 11% longer with CNWU than with CWU (ES = 0.62, respectively), and this effect was likely small. These findings suggest that power output at LT was higher in CNWU compared with NEU. Additionally, time to exhaustion at 120% of PPO was higher in CNWU compared with CWU and no different than NEU; these differences likely result in a small improvement in performance with CNWU versus CWU and NEU.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".