Skating mechanics of change-of-direction manoeuvres in ice hockey players
Bibliographic record
Abstract
Ice hockey requires rapid transitions between skating trajectories to effectively navigate about the ice surface. Player performance relates in large part to effective change-of-direction manoeuvres, but little is known about how those skills are performed mechanically and the effect of equipment design on them. The purpose of this study was to observe the kinetics involved in those manoeuvres as well as to compare whether kinetic differences may result between two skate models of varying ankle mobility. Eight subjects with competitive ice hockey playing experience performed rapid lateral (90°) left and right change-of-direction manoeuvres. Kinetic data were collected using force strain gauge transducers on the blade holders of the skates. Significantly greater forces were applied by the outside skate (50-70% body weight, %BW) in comparison to the inside skate (12-24%BW, p < 0.05). Skate model and turn direction had no main effect, though significant mixed interactions between leg side (inside/outside) with skate model or turn direction (p < 0.05) were observed, with a trend for left-turn dominance. This study demonstrates the asymmetric dynamic behaviour inherent in skating change-of-direction tasks.
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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 | 0.001 | 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".