Analysis of Jump Performance of World-Class Mogul Skiers Over an Olympic Quadrennial Cycle: A Case Study
Bibliographic record
Abstract
This case study examines the longitudinal jump data of 1 male and 1 female world-class mogul skier over the course of a quadrennial leading to the 2010 Winter Olympics. Between-subjects standard deviation, smallest worthwhile enhancement, % coefficient of variance, and effect size (ES) were calculated from team jump testing taking place immediately preceding the 2010 Winter Olympics, as this was deemed the point in the quadrennial that the athlete group would be most likely near their best performance. These data were then used to characterize the progression of explosive power of elite mogul skiers over an Olympic quadrennial. Jump data for both the male and the female athlete showed trivial to large improvements in jump performance from Q1 (quadrennial year 1) to Q2, variable changes in performance from Q2 to Q4, and an overall improvement (small to large ES) from Q1 to Q4. Explosive power is a critical component of performance for moguls, and an analysis of the group data (Canadian athletes 2006-2010) shows that of all performance markers, jump testing is the variable that clearly delineates between World Cup and developmental athletes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".