Forefoot and heel take-off strategies result in different distribution of lower extremity work during landings
Bibliographic record
Abstract
Previous research suggests that landing mechanics may be affected by the mechanics of the preceding jump take-off. The purpose of the present study was to investigate whether jump take-off mechanics influence the subsequent landing mechanics. Female volleyball (n = 17) and ice hockey (n = 19) players performed maximal vertical jumps with forefoot and heel take-off strategies. During forefoot and heel jumps, participants were instructed to shift their weight to their forefoot or heel, respectively, and push through this portion of the foot throughout the jump. Jump mechanics were examined using 3D motion analysis, where lower extremity net joint moment (NJM) work, NJM, and segment angles were compared between forefoot and heel jumps using multivariate ANOVA. During jump take-off, participants performed more positive ankle plantar flexor and knee extensor NJM work in forefoot compared to heel jumps (P < 0.05). From initial foot contact to foot flat, participants performed more negative ankle plantar flexor and hip extensor NJM work during heel compared to forefoot jumps (P < 0.05). The present results demonstrate that using a heel take-off strategy results in a different distribution of lower extremity NJM work and NJM during landing compared to landings following forefoot jumps.
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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) | 0.002 | 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".