Validation of the alpine skiing 90 seconds box jump field test and prediction of power output
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to validate the alpine ski racing 90 s box jump field-test (BJ90) with the 90 s Wingate test (W90).METHODS: Fifteen elite alpine ski racers were tested on the BJ90 (0.45 m × 0.75 m × 0.9 m) and the W90 (Ergomedic 894 E Monark; Monark AB, Varberg, Sweden) test in a random order. The number of hits on the BJ90 was written down every 15 s and for the W90 the load (7.5% of body weight) was manually dropped once the subjects reached maximum RPM without resistance. Correlations and partial correlations were calculated with a 2-tailed Pearson correlation analysis. A stepwise forward regression analysis was also performed.RESULTS: Results present significant (P<0.05) correlations and partial correlations adjusted for body weight between the total number of hits on the BJ90 and W90 peak W, peak W/kg, mean W and mean W/kg as well as with the number of hits and W90 Mean W and mean W/kg between each 15 s time slot. A linear regression predictive equation for mean relative power (W/kg) generated from the BJ90 was established: mean relative power (W/kg)=0.055*total hits+1.080 (r=0.79, P<0.001; total hits with mean W/kg). A stepwise forward regression equation for mean absolute power (W) generated from the BJ90 was also established: mean absolute power (W)=2.305*number of hits+5.081*body weight in kg-1 -169.8 (R=0.964, P<0.001, SEE=23.56).CONCLUSIONS: Practitioners could use the BJ90 to assess alpine skiers lower body power as previous research showed that both are correlated with performance and are able to discriminate skier level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".