Dental Microwear and Diets of Late Miocene Primates From Rudabánya, Hungary
Bibliographic record
Abstract
OBJECTIVES: This study focuses on a dental microwear texture analysis of European pliopithecids and dryopithecins from the Miocene primate site of Rudabánya, Hungary. The goal is to determine whether these taxa, found in part together in the same deposits, differed in their food preferences, or at least consumed, on a daily basis, in a manner that might have facilitated sympatry. MATERIALS AND METHODS: Here we report on a molar surface texture analysis of all available fossil primates from Rudabánya that preserve antemortem microwear. This includes both Anapithecus hernyaki (n = 14) and Rudapithecus hungaricus (n = 5, including one from Alsótelekes). Scanning confocal profilometry was used to generate point clouds, and texture complexity and anisotropy values were compared between the fossil taxa and contextualized with published data for an extant baseline series. RESULTS: Texture complexity and anisotropy values for both samples fall within the range of extant frugivorous primates. Further, while anisotropy does not differ between the fossil taxa, Rudapithecus has a significantly higher complexity average than Anapithecus. DISCUSSION: The difference in microwear texture complexity suggests that Rudapithecus individuals studied here consumed harder foods on average than did Anapithecus individuals did. This is consistent with the notion that dietary differences may have played a role in the niche separation of these taxa.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".