Biomechanics of slalom water skiing
Bibliographic record
Abstract
Water skiing has received little attention in research literature and has not utilized recent advancements in analysis technology like other highly dynamic sports. In this study, six advanced slalom skiers were recruited to test four different high-performance ski designs, with the goal being to detect performance differences achieved between ski designs and between skiers during slalom turns. To aid the analysis of the resulting activity data, a series of 11 quantitative performance parameters were defined and studied. Instrumentation included a skier-mounted, wireless, Global Positioning System sensor providing instantaneous skier velocity, a uniaxial force transducer providing rope load, and a wireless, inertial measurement unit attached to the skis to provide ski roll, ski acceleration and deceleration. Statistical analysis suggested that there was a difference in the average peak roll achieved between the skis, but was unable to suggest a difference between skis in the other performance parameters. In contrast, however, statistical analysis indicated that there was a difference in the performance achieved between the skiers, which is supported by their slalom course success rates. The identified performance parameters were effective at differentiating skier ability levels with the subject with the highest success rate among the top three highest scoring for 10 of 11 parameters and the subject with the lowest success rate was among the bottom 2 in all 11 parameters.
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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 | 0.000 | 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) | 0.002 | 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".