Functional diversification within and between muscle synergists during locomotion
Bibliographic record
Abstract
Locomotion arises from the complex and coordinated function of limb muscles. Yet muscle function is dynamic over the course of a single stride and between strides for animals moving at different speeds or on variable terrain. While it is clear that motor unit recruitment can vary between and within muscles, we know little about how work is distributed within and between muscles under in vivo conditions. Here we show that the lateral gastrocnemius (LG) of helmeted guinea fowl (Numida meleagris) performs considerably more work than its synergist, the medial gastrocnemius (MG) and that the proximal region of the MG (pMG) performs more work than the distal region (dMG). Positive work done by the LG was approximately twice that of the proximal MG when the birds walked at 0.5 ms -1, and four times when running at 2.0 m s-1. This is probably due to different moments at the knee, as well as differences in motor unit recruitment. The dMG performed less work than the pMG because its apparent dynamic stiffness was greater, and because it exhibited a greater recruitment of slow-twitch fibres. The greater compliance of the pMG leads to increased stretch of its fascicles at the onset of force, further enhancing force production. Our results demonstrate the capacity for functional diversity between and within muscle synergists, which increases with changes in gait and speed.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".