Different Lower-Limb Setup Positions Do Not Consistently Change Backstroke Start Time to 10 m
Bibliographic record
Abstract
Backstroke starts involve the athlete starting from a flexed position with their feet against the pool wall and then extending their ankles, knees, hips and back to push off; however, swimmers can start in different positions. The purpose of this study was to evaluate the performance impact of different knee extension angles in the setup position for a backstroke start. Ten backstroke swimmers completed maximum-effort starts in each of two setup positions: one with the knees maximally flexed, and one with the knees less flexed. The start handles and touchpad were instrumented with multi-axial force sensors. Activity of major hip and knee extensors was measured using surface electromyography. Body position in the sagittal plane was recorded using high-speed cameras. There was no overall difference in time to 10 m between the two conditions (p = 0.36, dz = 0.12), but some participants showed differences as large as 0.12 s in time to 10 m between start conditions. We observed that starts performed from a setup position with less knee flexion had an average 0.07 m greater head entry distance (p = 0.07, dz = 0.53), while starts from a setup position with maximal knee flexion had an average 0.2 m/s greater takeoff velocity (p = 0.02, dz = 0.78). Both head entry distance and takeoff velocity are related to start performance, suggesting each position may optimize different aspects of the backstroke start. Coaches should assess athletes individually to determine which position is optimal.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".