A new method to quantify mandibular corpus shape in extant great apes and its potential application to the hominoid fossil record
Bibliographic record
Abstract
OBJECTIVES: Mandibular corpus robusticity (corpus breadth/corpus height) is the most commonly utilized descriptor of the mandibular corpus in the great ape and hominin fossil records. As a consequence of its contoured shape, linear metrics used to characterize mandibular robusticity are inadequate to quantify the shape of the mandibular corpus. Here, we present an alternative to the traditional assessment of mandibular shape by analyzing the outline of the mandibular corpus in cross-section using landmarks and semilandmarks. MATERIALS AND METHODS: were quantified in a sample of hominoids and analyzed using generalized Procrustes analysis, Procrustes ANOVA, CVA, and cluster analysis. Corpus breadth and width were also collected from the same sample and analyzed using regression, ANOVA, and cluster analysis. RESULTS: Analysis of corpus outline shape revealed significant differences in mandibular corpus shape that are independent of size and sex at the genus level across hominoids. Cluster analysis based on the analysis of corpus outline shape results in almost all specimens grouping based on taxonomic affinity (99.28% correct classification). Comparison of these results to results using traditional measures of mandibular robusticity shows that analysis of the outline of the corpus in cross-section discriminate extant great apes more reliably. CONCLUSION: The strong taxonomic signal revealed by this analysis indicates that quantification of the outline of the mandibular corpus more fully captures mandibular corpus shape and offers the potential for greater power in discriminating among taxa in the hominoid fossil record.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".