Three-dimensional kinematics of the knee and ankle joints for three consecutive push-offs during ice hockey skating starts
Bibliographic record
Abstract
Little biomechanical research has been conducted recently on hockey skating despite the sport's worldwide appeal. One reason for this lack of biomechanical knowledge stems from the difficulty of collecting data. The lack of accuracy, the disputable realism of treadmills, and the large field of view required are some of the technical challenges that have to be overcome. The main objective of the current study was to improve our knowledge of the joint kinematics during the skating stroke. A second objective was to improve the data collection system we developed and the third was to establish if a kinematic progression exists in the hockey skating stroke similar to that in speed skating. Relative motions at the knee and ankle joints were computed using a joint coordinate system approach. The differences at the knee joints in push-offs indicated that the skating skill was progressively changing with each push-off. The relative stability of the ankle angles can be attributed to the design of the skate boots, which have recently become very rigid. Further research on ice hockey skating is warranted and should include more skaters and investigate the effect various starting strategies and variations in equipment have on skaters' 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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.001 | 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".