Whole‐bone shape of hominoid manual proximal phalanges
Bibliographic record
Abstract
Functional morphologists have long noted that skeletal adaptations in primate phalanges reflect locomotor behavior. While most studies have successfully used two-dimensional measurements to quantify general features of phalanx shape, a whole-bone three-dimensional analysis may better capture more subtle aspects of phalanx morphology that have not been quantifiable but are functionally meaningful. Here, we compare linear measurement (LM) and weighted spherical harmonic/sliding semilandmark (SPHARM-sliding) analyses of the manual third proximal phalanx (PP3) in extant hominoids (Homo, Pan, Gorilla, Pongo, Symphalangus, Hylobates; n = 292) and specimens attributed to Australopithecus afarensis (n = 2) and Homo neanderthalensis (n = 2). Morphological variation was summarized using principal component (PC) analysis. Differences between extant taxa were tested for using non-parametric MANOVAs (LM) and Procrustes distance resampling (SPHARM-sliding). Linear discriminant analyses (LDA) were performed using PC scores to assess whether the SPHARM-sliding or LM analysis better predicts group memberships of extant and fossil specimens. In both analyses, PC1 separates taxa along a locomotor gradient, and all extant genera are significantly different from one another (p ≤ 0.01) aside from Pongo versus Symphalangus in the LM analysis (p = 0.053). Only the SPHARM-sliding analysis found significant differences between taxa within each genus (p ≤ 0.04), and differences were even significant among Gorilla subspecies (p < 0.001). LDAs indicated that accuracy, separation effectiveness, and confidence were greater for the SPHARM-sliding analysis in predicting group membership among extant specimens, as well as fossil memberships to an extant group. Overall, results demonstrate that whole-bone, high-density landmark analyses can highlight nuanced features of PP3 morphology and may serve better for making inferences about fossils.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".