Effects of Varying Post Warm-Up Recovery Time on 200m Time Trial Swim Performance
Bibliographic record
Abstract
It has been proposed that warm up prior to athletic competition may enhance performance through affecting various physiological parameters. Little quantitative data exists on physiological responses to the warm up, and the data which has been reported is inconclusive. Similarly, it has been suggested that varying the recovery period following a standardized warm-up may affect subsequent performance. PURPOSE: To determine the effects of varying post warm-up recovery time on subsequent 200m swimming time trial. METHODS: Ten national caliber swimmers (5 male, 5 female) each swam a 1500m warm-up and performed a 200m time trial of specialty stroke following either 10 or 45 minutes of passive recovery. Subjects completed one time trial in each condition separated by one week in a counter – balanced order. Blood lactate and heart rate were measured immediately following warm-up, three minutes prior to race, immediately following race and three minutes post race. Rate of perceived exertion was measured immediately following warm-up and race. RESULTS: Time trial performance was significantly improved following 10 min as opposed to 45 min recovery (136.80 ±20.38s vs. 138.69 ± 20.32s, p<0.05). There were no significant difference between conditions for heart rate and blood lactate following the warm-up. However, pre-race heart rate was higher in the 10 min compared to the 45 min rest condition (109 ± 14bpmvs. 94 ±21 bpm, p<0.05). CONCLUSION: A post warm-up recovery time of 1 0min rather than 45min is more beneficial to 200m swimming time trial performance.
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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) | 0.003 | 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".