Variation in dental morphology and dietary breadth in primates and their kin
Bibliographic record
Abstract
Abstract Sympatric animals may consume diets of differing breadths as a strategy to reduce competition. Studying patterns of dietary breadth in extinct taxa is difficult because available data are generally limited to morphology. Dental topographic analysis (DTA) is useful for comparing occlusal morphology and allows for examination of dietary adaptations in extinct taxa. What remains unknown is how dental morphology, quantified using DTA, covaries with dietary breadth. The niche variation hypothesis (NVH) posits that taxa with broader ecological niches will be characterized by greater variability in morphology relative to specialized taxa. Therefore, we predict that taxa with greater dietary breadth will have more varied dental morphology compared to specialists as a result of the molar morphology of specialists being under greater genetic control relative to generalists, with specialists requiring teeth specially adapted to efficiently process a smaller range of food sources. We measured curvature, complexity, and relief of the M2 of 3 pairs of closely related euarchontan mammals (primates and treeshrews), with each taxon within a pair categorized as a generalist or specialist. Our results indicate that generalists do not consistently show greater variability in dental morphology compared to specialists among primates, but that atelids and treeshrews do generally follow the predictions of the NVH, with the caveat that our treeshrew sample is small. This suggests that while dietary specialists may be under greater genetic constraint with respect to their molar topography, a link between dietary breadth and dental form is not clear. Our study demonstrates that variation in dental topography does not necessarily reflect dietary breadth and highlights the fact that it is difficult to categorize even the most specialized primates (i.e. bamboo lemurs) as “dietary specialists.”
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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 | 0.000 |
| 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".