A multivariate approach to assess variation in tooth mineralization using free‐lived and captive‐raised chimpanzees (<i>P. troglodytes</i>)
Bibliographic record
Abstract
OBJECTIVES: Understanding variation in dental development among primates is important to accurately characterize species-specific sequences and times of tooth formation. Conventional approaches that summarize dental development data (i.e., dental maturity score, DMS) inherently omit information about the full range of variation in raw scores; thus, classic bivariate analyses are limited for exploring patterns of variation in detail. Here we report a new multivariate approach to simultaneously assess all raw dental scores, for all teeth, among all individuals for all groups, thus retaining much greater detail about population-specific patterns of variation. METHODS: We scored (0-12) permanent tooth mineralization using radiographs of mandibles of captive-raised known-age chimpanzees (Pan troglodytes; n = 114) and free-lived age-unknown conspecifics (n = 54). As a test of our method we also scored free-lived baboons (Papio anubis, n = 50) because of well-described contrasting patterns of permanent molar initiation between Papio and Pan. Using principal component analysis (PCA), we investigated how crypt, crown and root formation scores covaried with each other in all three genera, and with chronological age in captive chimpanzees. RESULTS: PCA successfully captured additional detail about variation among raw scores. Also, compared to DMS, PC1 scores correlated equivalently well with known ages but had lower prediction error. CONCLUSIONS: We found different patterns of variation in scores between younger juvenile free and captive chimpanzees but saw no wholesale differences between groups. Pan and Papio showed different patterns of variation, further validating this multivariate approach to visualize, quantify and compare raw dental score datasets among primate species.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".