Development of a comparative chimpanzee musculoskeletal glenohumeral model: implications for human function
Bibliographic record
Abstract
Modern human shoulder function is affected by the evolutionary adaptations that have occurred to ensure survival and prosperity of the species. Robust examination of behavioral shoulder performance and injury risk can be holistically improved through an interdisciplinary approach that integrates anthropology and biomechanics. Coordination of these fields can allow different perspectives to contribute to a more complete interpretation of biomechanics of the modern human shoulder. The purpose of this study was to develop a novel biomechanical and comparative chimpanzee glenohumeral model, designed to parallel an existing human glenohumeral model, and compare predicted musculoskeletal outputs between the two models. The chimpanzee glenohumeral model consists of three modules - an external torque module, a musculoskeletal geometric module and an internal muscle force prediction module. Together, these modules use postural kinematics, subject-specific anthropometrics, a novel shoulder rhythm, glenohumeral stability ratios, hand forces, musculoskeletal geometry and an optimization routine to estimate joint reaction forces and moments, subacromial space dimensions, and muscle and tissue forces. Using static postural data of a horizontal bimanual suspension task, predicted muscle forces and subacromial space were compared between chimpanzees and humans. Compared with chimpanzees, the human model predicted a 2 mm narrower subacromial space, deltoid muscle forces that were often double those of chimpanzees and a strong reliance on infraspinatus and teres minor (60-100% maximal force) over other rotator cuff muscles. These results agree with previous work on inter-species differences that inform basic human rotator cuff function and pathology.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".