Variation in enamel and dentine mineral concentration and density in primate molars
Bibliographic record
Abstract
Variation in enamel and dentine mineral concentration and total effective density can be reliably collected using Micro-CT scans. Both variables are suggested to reflect mechanical properties such as hardness and elastic modulus in dental tissues, meaning Micro-CT methods allow relative composition and mechanical properties to be collected non-destructively. 16 lower molars from 16 Catarrhine primates were Micro-CT scanned alongside hydroxyapatite phantoms using standardized settings and methods to calculate mineral concentration and total effective density. Mineral concentration, total effective density and thickness of dentine and enamel were calculated for four cusps, representing each 'corner' of the tooth and four lateral crown positions (i.e., mesial, buccal, lingual and distal). The results show mean mineral concentration and total effective density values were higher in areas of thicker enamel, while the opposite was observed for dentine. Buccal positions had significantly higher mineral concentration and total effective density values than lingual areas. Cuspal positions had higher mean values than lateral enamel, for both dentine (mineral concentration cuspal: 1.26 g/cm3; lateral: 1.20 g/cm3) and enamel (mineral concentration cuspal: 2.31 g/cm3; lateral: 2.25 g/cm3). Mesial enamel had significantly lower values than other locations. These common patterns across Catarrhine taxa may be linked to functional adaptations related to optimization of mastication and tooth protection. Variation in mineral concentration and total effective density may also be associated with wear and fracture patterns, and can be used as baseline information to investigate the effect of diet, pathological changes and aging on teeth through time.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".