Anatomical Network Analysis of Primate Skull Morphology
Bibliographic record
Abstract
A core question driving studies of primate evolution is how phenotypic variation in the skull arose without compromising vital functions and structures of the head. Alongside morphogenetic processes, mechanical forces influence head development, e.g. bone connections are primary growth sites and force-diffusors. Yet the influence that physical constraints (i.e., bone-bone contacts) have on the developmental plasticity and thus evolvability of skull form is unclear. Anatomical Network Analysis (AnNA) is a new way to query this influence by measuring presence, strength, direction, symmetry and density of connections among skull bones. Here we present AnNA of interaction among skull bones in twenty extant primate taxa, including humans, chosen to represent the phenotypic breadth of head variation. AnNA found that connectivity was more variable for the facial bones, which divided into three modules (midfacial, palatal, rostral) in non-human primates (a single module in humans); and more conserved for the cranial bones, which formed a single module in all primates but varied in specific bone-bone connections in Hominidae. Network models of the skull did not carry significant phylogenetic signals, stressing the influence of other factors in the evolution of skull anatomy. Our AnNA results also suggested that modularity and complexity do not evolve in a positive feedback relationship in the primate skull. Natural Sciences & Engineering Research Council of Canada (JCB); Howard University (RD); Cavanilles Institute of Biodiversity & Evolutionary Biology (BE-A, DR-G)
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".