What use are ontogenetic data anyway? Challenges in multivariate modelling of primate tooth formation
Bibliographic record
Abstract
Background The development of the permanent dentition provides a reliable substrate to infer ontogenetic patterns within and among species. Multivariate methods offer a promising approach to compare taxon-specific patterns.Aim This study used multivariate statistical approaches to compare ontogenetic patterns by more comprehensively quantifying variation in crypt and tooth formation scores for the permanent dentition in five catarrhine primate taxa, Homo sapiens, Pan paniscus, Pan troglodytes, Hylobates lar, and Papio anubis.Subjects and Methods Tooth formation was scored according to published standards for each specimen. Multivariate relationships between teeth were modelled according to a Bayesian multivariate cumulative probit model. Relationships among and between teeth were summarised with correlation matrices, variable loadings plots, and the Frobenius norm. Univariate boxplots were used to contextualise and check the biological salience of the multivariate results.Results H. sapiens results corroborate previous research and show a degree of modularity that separates early forming and later-forming teeth. All four other species may show broad correlative patterns, but clear biological patterns are masked due to small sample sizes and/or sample composition.Conclusion Even with careful application of statistical procedures, ontogenetic inferences are only as good as the data are comprehensive.
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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 | 0.072 | 0.203 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".