Neck Muscles in the Rhesus Monkey. I. Muscle Morphometry and Histochemistry
Bibliographic record
Abstract
Morphometric methods were used to describe the musculotendinous lengths, fascicle lengths, pennation angles, and cross-sectional areas of neck muscles in adult Macaca mulatta monkeys. Additionally, muscles were frozen, sectioned, and stained for ATPase activity to determine fiber-type composition. Individual rhesus muscles were found to vary widely in their degree of similarity to feline and human muscles studied previously. Suboccipital muscles and muscles supplied by the spinal accessory nerve were most similar to human homologs, whereas most other muscles exhibited architectural specializations. Many neck muscles were architecturally complex, with multiple attachments and internal aponeuroses or tendinous inscriptions that affected the determination of their cross-sectional areas. All muscles were composed of a mixture of type I, IIa, and IIb fiber types the relative proportions of which varied. Typically, head-turning muscles had lower proportions of type II (fast) fibers than homologous feline muscles, whereas extensor muscles contained higher proportions of type II fibers. The physical and histochemical specializations described here are known to have a direct bearing on functional properties, such as force-developing capacity and fatigue-resistance. These specializations must be recognized if muscles are to be modeled accurately or studied electrophysiologically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".