The adaptation of intercollegiate athletes to structured changes in the environment for closed sport skills
Bibliographic record
Abstract
Expert athletes are able to creatively vary the skills of their sport and adapt them to suit the current situation. The present study was designed to examine adaptation in the athletics events shot put and long jump. Six intercollegiate long jumpers completed 3 jumps from a longer set distance and 3 jumps from a shorter set distance, to assess variability in stride length and point of take off. Six shot putters completed 2 throws with their dominant arm, 3 throws with their non-dominant arm. Timing of phases, angle and height of release, distance and velocity of the throw were all assessed. In the 3 shorter jumps the average take-off distance from the board was 5.54 cm (±3.78); 12.09 cm (±8.62) for the longer run up distance. The final stride in the short run up was 1.69 m (±0.11); in the longer distance 1.71 m (±0.08). The dominant hand throws of the shot put produced a mean height of release of 1.93 m (±0.01); angle of release 41.50°(±2.12); glide step distance of 1.03 m (±3.55); glide step time of 1.25 s (±0.12); and a release phase time of 0.29 s (±0.02). The non-dominant hand throws of the shot put athletes produced a mean height of release of 1.68 m (±0.20); angle of release 26.00°(±2.65); glide step distance of 0.83 m (±15.17); glide step time of 1.40 s (±0.07); and a release phase time of 0.41 s (±0.14). Results were compared to athletes' with intellectual disability to examine the relationship of cognitive ability to adaptation in closed sport skills.Acknowledgments: Manitoba Health Research Council
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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 | 0.002 |
| 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) | 0.002 | 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".