First anatomical network analysis of fore- and hindlimb musculoskeletal modularity in bonobos, common chimpanzees, and humans
Bibliographic record
Abstract
Studies of morphological integration and modularity, and of anatomical complexity in human evolution typically focus on skeletal tissues. Here we provide the first network analysis of the musculoskeletal anatomy of both the fore- and hindlimbs of the two species of chimpanzee and humans. Contra long-accepted ideas, network analysis reveals that the hindlimb displays a pattern opposite to that of the forelimb: Pan big toe is typically seen as more independently mobile, but humans are actually the ones that have a separate module exclusively related to its movements. Different fore- vs hindlimb patterns are also seen for anatomical network complexity (i.e., complexity in the arrangement of bones and muscles). For instance, the human hindlimb is as complex as that of chimpanzees but the human forelimb is less complex than in Pan. Importantly, in contrast to the analysis of morphological integration using morphometric approaches, network analyses do not support the prediction that forelimb and hindlimb are more dissimilar in species with functionally divergent limbs such as bipedal humans.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".