Seasonal Mortality Patterns in Primates: Implications for the Interpretation of Dental Microwear
Bibliographic record
Abstract
The microscopic traces of use wear on teeth have been extensively studied to provide information that will assist in elucidating the dietary habits of extinct hominin species. It has been amply documented that dental microwear provides information pertaining to diet for living animals, where there is a strong and consistent association between dental microwear patterns and different types of foods that are chewed. The details of occlusal surface wear patterns are capable of distinguishing among diets when the constituent food items differ in their fracture properties. For example, the microwear traces left on the teeth of mammals that crush hard, brittle foods such as nuts are generally dominated by pits, whereas traces left on the teeth of mammals that shear tough items such as leaves tend to be characterized by scratches. These microwear features result from and thus record actual chewing events. As such, microwear patterns are expected to be variably ephemeral, as individual features are worn away and replaced or overprinted by others as the tooth wears down in subsequent bouts of mastication. Indeed, it has been demonstrated, both in the laboratory and the wild, that short-term dietary variation can result in the turnover of microwear. Because occlusal microwear potentially reflects an individual's diet for a short time (days, weeks, or months, depending on the nature of the foods being masticated), tooth surfaces sampled at different times will display differences that relate to temporal (for example, seasonal) differences in diet.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".