PERFORMANCE AND TALENT IDENTIFICATION OF FEMALE ICE HOCKEY PLAYERS
Bibliographic record
Abstract
Female ice hockey continues to take a higher profile after two Winter Olympics in which women have played for medals. Canadian and American University, and youth hockey, are flourishing. Female ice hockey has also been enhanced by a professional league in Canada. As such, ongoing investigations of the physical performance characteristics of female ice hockey players are essential to the continued improvement of this wonderful game. Dr. Geithner will present data that show female ice hockey players, across a wide range of ages, tend to be tall, heavy, and heavy for their height. Body composition and physique vary with both age and playing ability, and age at menarche varies with the latter. Physical characteristics of players are generally consistent with the sport's requirements, probably reflecting a selective factor for performance. She will present other pertinent data on female hockey anthropometrics. Dr. Bracko will present data that show 40 yard dash and vertical jump height are predictors of skating speed and on-ice anaerobic capacity, the differences between elite and non-elite female ice hockey players are age, skating speed, and on-ice fitness, and the on and off-ice fitness of university players stay the same during the season. He will present other pertinent data on the performance characteristics of players aged 8 - 35. Dr. Rundell will present the testing and training plans for the U.S. Women's Ice Hockey team during the year preceding the Salt Lake Olympics: a retrospective analysis of what went right, and what went wrong, will be discussed. He will provide a complete physiological profile of the world's top female ice hockey players and present the training involved to achieve top physical conditioning.
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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 | 0.001 |
| 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) | 0.004 | 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".