A geometric morphometric approach to investigate primate proximal phalanx diaphysis shape
Bibliographic record
Abstract
Abstract Current approaches to quantify phalangeal curvature assume that the long axis of the bone's diaphysis approximates the shape of a portion of a circle (included angle method) or a parabola (second‐degree polynomial method). Here we developed, tested, and employed an alternative geometric morphometrics‐based (GM) approach to quantify diaphysis shape of proximal phalanges in humans, apes and monkeys with diverse locomotor behaviors. One hundred landmarks of the central longitudinal axis were extracted from 3D surface models and analyzed using 2DGM methods, including generalized Procrustes analyses. Principal components analyses were performed and PC1 scores (>80% of variation) represented the dorsopalmar shape of the bone's central longitudinal axis and separated taxa consistently and in accord with known locomotor behavioral profiles. The most suspensory taxa, including orangutans, hylobatids and spider monkeys, had significantly lower PC1 scores reflecting the greatest amounts of phalangeal curvature. In contrast, bipedal humans and the quadrupedal cercopithecoid monkeys sampled (baboons, proboscis monkeys) exhibited significantly higher PC1 scores reflecting flatter phalanges. African ape (gorillas, chimpanzees and bonobos) phalanges fell between these two extremes and were not significantly different from each other. PC1 scores were significantly correlated with both included angle and the a coefficient of a second‐degree polynomial calculated from the same landmark dataset, but had a significantly higher correlation with included angles. Our alternative approach for quantifying diaphysis shape of proximal phalanges to investigate dorsopalmar curvature is replicable and does not assume a priori either a circle or parabola model of shape, making it an attractive alternative compared with existing methodologies.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".