Bibliographic record
Abstract
To interpret the movement strategies employed in locomotion, it is necessary to understand the source of metabolic cost. Muscles must consume metabolic energy to do work, but also must consume energy to generate force. The energy lost during steady locomotion and, hence, the amount of mechanical work muscles need to perform to replace it can be reduced and, in theory, even eliminated by elastically storing and returning some portion of this energy via the tendons. However, even if muscles do not need to perform any mechanical work, they still must generate sufficient force to tension tendons and support body weight. This study shows that the metabolic cost per hop of human hopping can largely be explained by the cost of producing force over the duration of a hop. Metabolic cost determined via oxygen consumption is compared with theoretical predictions made using a number of different cost functions that include terms for average muscle work, force, force rate and impulse (time integral of muscle force). Muscle impulse alone predicts metabolic cost per hop as well as more complex functions that include terms for muscle work, force and force rate, and explains a large portion (92%) of the variation in metabolic cost per hop. This is equivalent to 1/effective mechanical advantage, explaining a large portion (66%) of the variation in metabolic cost per time per unit body weight. This result contrasts with studies that suggest that muscle force rate or muscle force rate per time determines the metabolic cost per time of force production in other bouncing gaits such as running.
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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 | 0.002 |
| 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 | 0.001 | 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".