The dynamic limits of hop height: Biological actuator capabilities and mechanical requirements of task produce incongruity between one- and two-legged performance
Bibliographic record
Abstract
The maximum hop height attainable for a given hop frequency falls well below the theoretical limit dictated by gravity, h = g/8f(2). However, maximum hop height is proportional to 1/f(2), suggesting that ground reaction force and, hence, force production capabilities of the leg muscles limit human hopping performance. Curiously, during one-legged hopping, subjects were able to produce substantially more than 50% the ground reaction force produced during two-legged maximum height hopping-66% on average and as much as 90% the total force produced during two-legged hopping. This implies that two legs together should be able to produce an average of 1.32 times and as much as 1.8 times the force actually measured during two-legged maximum height hopping. Why were our subjects unable to access this extra force capacity when hopping on two legs? Here, we show that this apparent bilateral deficit and other features of maximum height hopping can be explained by the interaction of the mechanical requirements of hopping with the force-velocity and force-length relationships that dictate the force production capacity of the leg muscles. Identifying the factors that limit performance in hopping provides an opportunity to understand how functional limits are determined in more complex activities such as running and jumping.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".