Anterior dentary shape as an indicator of diet in ruminant artiodactyls
Bibliographic record
Abstract
Several studies have quantified selective capability in artiodactyls using skeletal correlates such as the premaxillary shape index (PSI), incisor arcade breadth (IAB), incisor arcade curvature (DOC), and incisor width ratio (IWR). These metrics are limited in their applicability because they do not account for the potential importance of muzzle length. Herein we apply a new method for the quantification of muzzle shape (the dentary shape index [DSI]) that combines dentary length and width. We find that browsers possess the longest, narrowest muzzles, whereas the muzzles of grazers are comparatively short and wide. Discriminant function analysis demonstrates that DSI yields the highest rate of correct dietary classification of all muzzle shape proxies proposed to date. Generalized estimating equations and phylogenetically independent contrasts show that the shape of the anterior dentary (DSI) is significantly correlated with both diet and premaxillary shape (PSI) across the cetartiodactyl tree. We suggest that the match between premaxillary and anterior dentary shape is important in cropping but it is the shape of the lower incisor arcade that is under direct dietary selection. We conclude that the length and width of the anterior dentary are functional determinants of selectivity in ruminant artiodactyls and that these traits have potentially evolved in response to selection for efficient feeding as it is related to the different dietary requirements of browsers and grazers.
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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 | 0.002 | 0.001 |
| 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".