Convergence, divergence, and macroevolutionary constraint as revealed by anatomical network analysis of the squamate skull, with an emphasis on snakes
Bibliographic record
Abstract
Traditionally considered the earliest-diverging group of snakes, scolecophidians are central to major evolutionary paradigms regarding squamate feeding mechanisms and the ecological origins of snakes. However, quantitative analyses of these phenomena remain scarce. Herein, we therefore assess skull modularity in squamates via anatomical network analysis, focusing on the interplay between 'microstomy' (small-gaped feeding), fossoriality, and miniaturization in scolecophidians. Our analyses reveal distinctive patterns of jaw connectivity across purported 'microstomatans', thus supporting a more complex scenario of jaw evolution than traditionally portrayed. We also find that fossoriality and miniaturization each define a similar region of topospace (i.e., connectivity-based morphospace), with their combined influence imposing further evolutionary constraint on skull architecture. These results ultimately indicate convergence among scolecophidians, refuting widespread perspectives of these snakes as fundamentally plesiomorphic and morphologically homogeneous. This network-based examination of skull modularity-the first of its kind for snakes, and one of the first to analyze squamates-thus provides key insights into macroevolutionary trends among squamates, with particular implications for snake origins and 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 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) | 0.001 | 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".