Time Decay in the Performance Benefits From Repeated-Sprint Training in Hypoxia in World-Class Short-Track Speed Skaters
Bibliographic record
Abstract
PURPOSE: In short-track speed skating, athletes need to possess specific physiological capabilities to perform explosive starts and to finish races with faster lap times than their opponents. Repeated-sprint training in hypoxia (RSH) can enhance repeated-sprint ability and high-intensity performance. This study aimed to evaluate the relationship between on- and off-ice performance indicators for training and testing purposes and how these are optimized with RSH in world-class short-track speed skaters including world and Olympic champions. METHODS: RSH training was administered for 3 consecutive weeks, 3 times per week, at 3500 m of simulated altitude. Testing sessions (on-ice: 3-lap, 27-lap; off-ice: cycling incremental test, 7-s and 30-s Wingate) were performed immediately before and 2 and 4 weeks after RSH to determine the time course of decay. RESULTS: On-ice top speed showed a small and possibly beneficial change of ∼0.9% for the women and large and almost certain ∼0.7% improvement for the men 2 weeks post-RSH. Cycling peak power showed a moderate and probable ∼5.4% improvement for the men 2 weeks after RSH. These adaptations reverted to baseline 4 weeks post-RSH. Wingate average power showed a small and possibly beneficial gain (∼3.4%) in performance 4 weeks post-RSH. CONCLUSION: Although scientific controls could not be added due to the extremely high caliber of these athletes and low sample size of the national team, this study suggests that cycling RSH can be added immediately after on-ice training and can transfer into meaningful improvements on the ice in both male and female skaters.
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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 | 0.000 |
| 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) | 0.001 | 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".