Mammalian molar complexity follows simple, predictable patterns
Bibliographic record
Abstract
Identifying developmental explanations for the evolution of complex structures like mammalian molars is fundamental to studying phenotypic variation. Previous study showed that a "morphogenetic gradient" of molar proportions was explained by a balance between inhibiting/activating activity from earlier developing molars, termed the inhibitory cascade model (ICM). Although this model provides an explanation for variation in molar proportions, what remains poorly understood is if molar shape, or specifically complexity (i.e., the number of cusps, crests), can be explained by the same developmental model. Here, we show that molar complexity conforms to the ICM, following a linear, morphogenetic gradient along the molar row. Moreover, differing levels of inhibiting/activating activity produce contrasting patterns of molar complexity depending on diet. This study corroborates a model for the evolution of molar complexity that is developmentally simple, where only small-scale developmental changes need to occur to produce change across the entire molar row, with this process being mediated by an animal's ecology. The ICM therefore provides a developmental framework for explaining variation in molar complexity and a means for testing developmental hypotheses in the broader context of mammalian 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".