Buccal dental-microwear and dietary ecology in a free-ranging population of mandrills (Mandrillus sphinx) from southern Gabon
Bibliographic record
Abstract
Analyses of dental micro- and macro-wear offer valuable information about dietary adaptations. The buccal surface of the teeth does not undergo attrition, indicating that dental microwear may directly inform about food properties. Only a few studies have, however, investigated the environmental and individual factors involved in the formation of such microwear in wild animals. Here, we examine variation of buccal microwear patterns of mandibular molars in a large free-ranging population of mandrills (Mandrillus sphinx). We first explore the influence of seasonality and individual's sex, age and tooth macrowear-expressed as the percent of dentine exposure (PDE)-on six microwear variables. Second, we analyze the interplay between individual's diet and PDE. In a last analysis, we revisit our results on mandrills in the light of other primate's microwear studies. We show that the average buccal scratch length and the frequency of vertical buccal scratches are both higher during the long dry season compared to the long rainy season, while we observe the inverse relationship for disto-mesial scratches. In addition, females present more disto-mesial scratches than males and older individuals present higher scratch density, a greater proportion of horizontal scratches but a lower proportion of vertical scratches than young animals. PDE yields similar results than individual's age confirming earlier results in this population on the relationship between age and tooth macrowear. Because seasonality and individual characteristics are both known to impact mandrills' diet in the study population, our results suggest that buccal microwear patterns may inform about individual feeding strategies. Furthermore, PDE increases with the consumption of potentially abrasive monocotyledonous plants, independently of the individuals' age, although it is not affected by food mechanical properties. Finally, buccal scratch densities by orientation appear as relevant proxies for discriminating between different primate taxa.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".