Pacing Strategy, Muscle Fatigue, and Technique in 1500-m Speed-Skating and Cycling Time Trials
Bibliographic record
Abstract
PURPOSE: To evaluate pacing behavior and peripheral and central contributions to muscle fatigue in 1500-m speed-skating and cycling time trials when a faster or slower start is instructed. METHODS: Nine speed skaters and 9 cyclists, all competing at regional or national level, performed two 1500-m time trials in their sport. Athletes were instructed to start faster than usual in 1 trial and slower in the other. Mean velocity was measured per 100 m. Blood lactate concentrations were measured. Maximal voluntary contraction (MVC), voluntary activation (VA), and potentiated twitch (PT) of the quadriceps muscles were measured to estimate central and peripheral contributions to muscle fatigue. In speed skating, knee, hip, and trunk angles were measured to evaluate technique. RESULTS: Cyclists showed a more explosive start than speed skaters in the fast-start time trial (cyclists performed first 300 m in 24.70 ± 1.73 s, speed skaters in 26.18 ± 0.79 s). Both trials resulted in reduced MVC (12.0% ± 14.5%), VA (2.4% ± 5.0%), and PT (25.4% ± 15.2%). Blood lactate concentrations after the time trial and the decrease in PT were greater in the fast-start than in the slow-start trial. Speed skaters showed higher trunk angles in the fast-start than in the slow-start trial, while knee angles remained similar. CONCLUSIONS: Despite similar instructions, behavioral adaptations in pacing differed between the 2 sports, resulting in equal central and peripheral contributions to muscle fatigue in both sports. This provides evidence for the importance of neurophysiological aspects in the regulation of pacing. It also stresses the notion that optimal pacing needs to be studied sport specifically, and coaches should be aware of this.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".