Movement mechanics as a determinate of muscle structure, recruitment and coordination
Bibliographic record
Abstract
During muscle contractions, the muscle fascicles may shorten at a rate different from the muscle-tendon unit, and the ratio of these velocities is its gearing. Appropriate gearing allows fascicles to reduce their shortening velocities and allows them to operate at effective shortening velocities across a range of movements. Gearing of the muscle fascicles within the muscle belly is the result of rotations of the fascicles and bulging of the belly. Variable gearing can also occur as a result of tendon length changes that can be caused by changes in the relative timing of muscle activity for different mechanical tasks. Recruitment patterns of slow and fast fibres are crucial for achieving optimal muscle performance, and coordination between muscles is related to whole limb performance. Poor coordination leads to inefficiencies and loss of power, and optimal coordination is required for high power outputs and high mechanical efficiencies from the limb. This paper summarizes key studies in these areas of neuromuscular mechanics and results from studies where we have tested these phenomena on a cycle ergometer are presented to highlight novel insights. The studies show how muscle structure and neural activation interact to generate smooth and effective motion of the body.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".