Lower-limb extension power: How well does it predict short distance speed skating performance?
Bibliographic record
Abstract
{\it Purpose:} This study was aimed to explore the relationship between lower limb extension power measured by isokinetic knee extensions (IK) and vertical jumps performed on a force plate (VJ) and speed skating (SS) sprint power measured by a laser device. {\it Methods:} Twenty elite short- and long-track speed skaters performed 100 m sprints followed by VJ and IK trials. Power-time curves were calculated off-line. Pearson correlation coefficients were used to determine the degree of association between the variables. {\it Results:} SS sprint power correlates strongly with VJ power(r=0.870; p<0.001) while IK power showed a weaker but significant correlation to both (r=0.707 and r=0.706, respectively; p<0.01). As expected, SS times at 15 m and 100 m were inversely associated with SS sprint power (r=-0.818 and r=-0.909; p<0.001) and VJ power (r=-0.730 and r=-0.763; p<0.001), and to a lesser degree with IK power (r=-0.602; r=-0.618; p<0.01). {\it Conclusion:} The analyses differentiate between methods of estimating power in speed skaters, and show a strong relationship between initial SS performance and muscular power. Given that 100 m split times strongly relate to final 500 m results (r=0.972; p<0.001, N=332), it is reasonable to believe that an initial power and a stable peak speed before the first curve may lead to achieving the winning edge in short SS events. A finding of particular interest is that isokinetic power results are correlated significantly with the practical outcomes of the performance in spite of the high specificity of the isokinetic testing method.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".