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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".