Water skiing biomechanics: a study of advanced skiers
Bibliographic record
Abstract
Six advanced slalom skiers were recruited to test four different ski designs over the summer months of 2010. An axial load transducer, boat global positioning system and skier global positioning system were used to calculate rope load and velocity for the skier during the deep water start and cutting portion of a slalom run. The methodology was designed where possible to reduce the uncontrollable factors that present themselves in the natural environment of slalom water skiing. There was statistical evidence to suggest that there was a difference in the average peak rope load produced between the skis during cutting. The typical average peak rope load and skier velocity, for an advanced skier, during the cutting portion of a ski run is in the range of 1.41–2.74 times body weight. The instrumentation was unable to provide enough evidence to suggest that there was a difference in the peak skier velocity during cutting or peak rope load during deep water starts. The typical average peak skier velocity while cutting, and rope load, during a deep water start, for an advanced skier, was in the range of 114–135% of boat speed and 1.74–2.74 times body weight. Furthermore, there was a statistical difference in the overall performance of the skiers participating in the study. The analysis techniques utilized in this study have the potential of providing more quantitative performance evaluation data than is currently available for water ski product development and coaching, and should prove useful for future performance-driven development and coaching.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
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".