Are more diverse parts of the mammalian skull more labile?
Bibliographic record
Abstract
Morphological variation is unevenly distributed within the mammalian skull; some of its parts have diversified more than others. It is commonly thought that this pattern of variation is mainly the result of the structural organization of the skull, as defined by the pattern and magnitude of trait covariation. Patterns of trait covariation can facilitate morphological diversification if they are aligned in the direction of selection, or these patterns can constrain diversification if oriented in a different direction. Within this theoretical framework, it is thought that more variable parts possess patterns of trait covariation that made them more capable of evolutionary change, that is, are more labile. However, differences in the degree of morphological variation among skull traits could arise despite variation in trait lability if, for example, some traits have evolved at a different rate and/or undergone stabilizing selection. Here, we test these hypotheses in the mammalian skull using 2D geometric morphometrics to quantify skull shape and estimating constraint, rates of evolution, and lability. Contrary to the expectations, more variable parts of the skull across mammalian species are less capable of evolutionary change than are less variable skull parts. Our results suggest that patterns of morphological variation in the skull could result from differences in rate of evolution and stabilizing selection.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".