Athletic Profile of Alpine Ski Racers: A Systematic Review
Bibliographic record
Abstract
Ferland, PM and Comtois, AS. Athletic profile of alpine ski racers: a systematic review. J Strength Cond Res 32(12): 3591-3600, 2018-The purpose of this study was to review all anthropometric and physical test results performed on alpine ski racers that were published in the scientific literature to build an athletic profile specific to the skier's sex and level. Four electronic databases were systematically searched using the following key words: alpine, skiing, and physiology. The manual search was performed through the reference list of all suitable publications, the author's personal collection, and the proceedings of the International Congresses on Science and Skiing. The search and selection strategy permitted to gather data from 28 peer-reviewed publications that were collected on a total of 1,107 skiers coming from 11 different countries. Results of this study present the athletic profile and also review the different testing protocols. Findings show that men generally present higher test results than women and that higher-level ski racers generally present higher test results than lower-level ski racers. The present review should serve as guidelines for professionals working with alpine ski racers because most of the factors presented in the athletic profile have previously been shown to be related with performance. Further research should include more details on the testing protocols used, be directed toward female athletes, and present results from groups of athletes of the same sex and clearly identified as established at a certain level. These measures could help support further theoretical investigations.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".