Bibliographic record
Abstract
impact on vertical jump performance; however the reason for this improvement isunknown. In order to maximize jump performance using apparel, information oncertain aspects of the jumping mechanism must be gathered.Purpose: The aim of this study was to examine the influence of hip joint flexionangle on jump height and to determine if athletes naturally attain the optimal hip jointangle.Methods: Ten subjects performed counter-movement jumps in three conditions. Thefirst set of trials was performed at the subjects’ self-selected hip flexion angle. Motioncapture was used to determine the hip flexion angle after each jump and peak heightwas measured with the Vertec jump tester. The next two conditions were performed at(approx.) 10 degrees of hip flexion less than and greater than the control condition.Inverse dynamics was used to analyze the joint moments and analog force plate datawas used to analyze ground reaction force and impulse.Results: Differences in jump height were observed for each condition. The highestaverage jump height was achieved during the control condition, with the abovecondition (shallower squat during countermovement) a close second and the belowcondition had the lowest average jump height. The mean ground reaction force forthe above condition followed the same pattern as the control condition results butshowed a higher magnitude, while the below ground reaction force was substantiallylower than the control results. The peak hip extension moment was consistently thegreatest in magnitude for the below condition and smallest for the above condition.
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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 | 0.000 | 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) | 0.006 | 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".