Morphometry of <i>macaca mulatta</i> forelimb. III. moment arm of shoulder and elbow muscles
Bibliographic record
Abstract
The conversion of muscle activity into smooth, purposeful movement of the limb depends complexly on the morphometry of muscles and their mechanical action on the skeleton. Although nonhuman primates are common subjects in motor control experiments (Scott [2000] Can J Physiol Pharmacol 78:923-933), little information is available on the morphometric properties of their upper limbs. One key variable is muscle moment arm, or mechanical advantage, which defines how linear motion or force of a muscle is translated into angular motion or torque at a joint. This study reports moment arm values with respect to joint angle (flexion/extension) of 14 muscles spanning the shoulder and elbow in Macaca mulatta. The magnitude of moment arm values ranged widely across muscles. In some muscles mechanical advantage remained constant with joint angle, whereas the moment arm of others varied strongly. The angle (Theta(f)(o)) at which optimal fascicle length (L(f)(o)) occurred showed strong trends, where the elbow-spanning muscles had Theta(f)(o) values clustered at mid-flexion and the shoulder musculature Theta(f)(o) values tended to be grouped around the neutral joint angle of 0 degrees. Estimates of peak muscle torque for flexor and extensor muscle groups at each joint were surprisingly similar in both magnitude and dependency on joint angle. The present study, along with the previous two in this series (Cheng and Scott [2000] J Morphol 245:206-224; Singh et al. [2002] J Morphol 251:323-332), provides a comprehensive description of the morphology of the proximal portion of the limb suitable for the development of a musculoskeletal model of the M. mulatta upper limb.
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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 | 0.000 |
| 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 | 0.000 |
| 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".