Morphological integration of anatomical, developmental, and functional postcranial modules in the crab‐eating macaque (<i>Macaca fascicularis</i>)
Bibliographic record
Abstract
OBJECTIVES: Integration and modularity reflect the coordinated action of past evolutionary processes and, in turn, constrain or facilitate phenotypic evolvability. Here, we analyze magnitudes of integration in the macaque postcranium to test whether 20 a priori defined modules are (1) more tightly integrated than random sets of postcranial traits, and (2) are differentiated based on mode of definition, with developmental modules expected to be more integrated than functional or anatomical modules. MATERIALS AND METHODS: The 3D morphometric data collected for eight limb and girdle bones for 60 macaques were collated into anatomical, developmental, and functional modules. A resampling technique was used to create random samples of integration values for each module for statistical comparison. RESULTS: Our results found that not all a priori defined modules were more strongly integrated than random samples of postcranial traits and that specific types of modules did not present consistent patterns of integration. Rather, girdle and joint modules were consistently less integrated than limb modules, and forelimb elements were less integrated than hindlimbs. DISCUSSION: The results suggest that morphometrically complex modules tend to be less integrated than simple limb bones, irrespective of the number of available traits. However, differences in integration of the fore- and hindlimb more likely reflects the multitude of locomotory, feeding, and social functions involved. It remains to be tested whether patterns of integration identified here are primate universals, and to what extent they vary depending on phylogenetic or functional factors.
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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 | 0.000 | 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".