Does Warm-Up Have a Beneficial Effect on 100-m Freestyle?
Bibliographic record
Abstract
PURPOSE: To investigate the effect of warm-up on 100-m swimming performance. METHODS: Twenty competitive swimmers (with a training frequency of 8.0 ± 1.0 sessions/wk) performed 2 maximal 100-m freestyle trials on separate days, with and without prior warm-up, in a counterbalanced and randomized design. The warm-up distance totaled 1000 m and replicated the swimmers' usual precompetition warm-up strategy. Performance (time), physiological (capillary blood lactate concentrations), psychophysiological (perceived exertion), and biomechanical variables (distance per stroke, stroke frequency, and stroke index) were assessed on both trials. RESULTS: Performance in the 100-m was fastest in the warm-up condition (67.15 ± 5.60 vs 68.10 ± 5.14 s; P = .01), although 3 swimmers swam faster without warm-up. Critical to this was the 1st 50-m lap (32.10 ± 2.59 vs 32.78 ± 2.33 s; P < .01), where the swimmers presented higher distance per stroke (2.06 ± 0.19 vs. 1.98 ± 0.16 m; P = .04) and swimming efficiency compared with the no-warm-up condition (stroke index 3.46 ± 0.53 vs 3.14 ± 0.44 m2 · c1 · s1; P < .01). Notwithstanding this better stroke-kinematic pattern, blood lactate concentrations and perceived exertion were similar between trials. CONCLUSIONS: These results suggest that swimmers' usual warm-up routines lead to faster 100-m freestyle swimming performance, a factor that appears to be related to better swimming efficiency in the 1st lap of the race. This study highlights the importance of performing swimming drills (for higher distance per stroke) before a maximal 100-m freestyle effort in similar groups of swimmers.
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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 | 0.000 | 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) | 0.000 | 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".