The effect of high wear diets on the relative pulp volume of the lower molars
Bibliographic record
Abstract
OBJECTIVES: One role of dental pulp is in the upkeep and maintenance of dentine. Under wear, odontoblasts in the pulp deposit tertiary dentine to ensure the sensitive internal dental tissues are not exposed and vulnerable to infection. It follows that there may be an adaptive advantage for increasing molar pulp volume in anthropoid primate taxa that are prone to high levels of wear. The relative volume of dental pulp is therefore predicted to covary with dietary abrasiveness (in the sense of including foods that cause high degrees of wear). MATERIALS AND METHODS: We examined relatively unworn lower second molars in pairs of species of extant hominoids, cebids, and pitheciids that vary in the abrasiveness of their diet (n = 36). Using micro-CT scans, we measured the percent of tooth that is pulp (PTP) as the ratio of pulp volume to that of the total volume of the tooth. RESULTS: We found that in each pair of species, the taxa that consume a more abrasive diet had a significantly higher PTP than the closely related taxa that consume a softer diet. CONCLUSIONS: Our results point to an adaptive mechanism in the molars of taxa that consume abrasive diets and are thus subject to higher levels of wear. Our results provide additional understanding of the relationship between dental pulp and diet and may offer insight into the diet of extinct taxa such as Paranthropus boisei or into the adaptive context of the taurodont molars of Neanderthals.
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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 | 0.001 |
| 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".