The Effect of On-Hill Active Recovery Performed Between Runs on Blood Lactate Concentration and Fatigue in Alpine Ski Racers
Bibliographic record
Abstract
Alpine skiing is a high-intensity intermittent sport that results in lactate accumulation and muscle acidosis, which has been shown to contribute to peripheral neuromuscular fatigue. Active recovery influences the removal of lactate from the muscle and blood by maintaining blood flow to fatigued muscles and enhancing aerobic utilization of lactate by nonfatigued tissues. The purpose of this study was to investigate the effect of on-hill active recovery on blood lactate concentration in alpine skiers. Fourteen highly trained alpine skiers (7 women, 7 men) completed 8 training runs in a 45-gate slalom or a 25-gate giant slalom corridor at 2,600 m above sea level. Skiers were randomized to active (ACT) or static recovery (CON) performed at the top of each run. Blood lactate concentration and perceived fatigue were recorded at the top and bottom of each run. Performance was measured by time to complete each training run and rate of incomplete runs. A significant time (p < 0.01) and interaction (p = 0.001) effect was observed for blood lactate concentration measured at the top, with ACT being associated with significantly lower values. A significant time effect (p < 0.001) was observed for blood lactate concentration measured at the bottom. Training run completion time was longer (p ≤ 0.05), and higher rate of incomplete runs were observed in the CON group, despite no between-group differences in rating of perceived fatigue. On-hill active recovery performed between runs promotes blood lactate clearance in alpine skiers and is associated with delayed fatigue as indicated by faster training runs and fewer incomplete runs.
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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) | 0.001 | 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".