Evaluating modularity in the hominine skull related to feeding biomechanics
Bibliographic record
Abstract
OBJECTIVES: Modular architecture of traits in complex organisms can be important for morphological evolution at micro- and sometimes macroevolutionary scales as it may influence the tempo and direction of changes to groups of traits that are essential for particular functions, including food acquisition and processing. We tested several distinct hypotheses about craniofacial modularity in the hominine skull in relation to feeding biomechanics. MATERIALS AND METHODS: First, we formulated hypothesized functional modules for craniofacial traits reflecting specific demands of feeding biomechanics (e.g., masseter leverage/gape or tooth crown mechanics) in Homo sapiens, Pan troglodytes, and Gorilla gorilla. Then, the pattern and strength of modular signal was quantified by the covariance ratio coefficient and compared across groups using covariance ratio effect size. Hierarchical clustering analysis was then conducted to examine whether a priori-defined functional modules correspond to empirically recovered clusters. RESULTS: There was statistical support for most a priori-defined functional modules in the cranium and half of the functional modules in the mandible. Modularity signal was similar in the cranium and mandible, and across the three taxa. Despite a similar strength of modularity, the empirically recovered clusters do not map perfectly onto our priori functional modules, indicating that further work is needed to refine our hypothesized functional modules. CONCLUSION: The results suggest that modular structure of traits in association with feeding biomechanics were mostly shared with humans and the two African apes. Thus, conserved patterns of functional modularity may have facilitated evolutionary changes to the skull during human evolution.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".