Looking beyond phalangeal length and curvature: Functional correlation between manual phalangeal articular and collateral ligamentous morpohology and anthropoid locomotor adaptations
Bibliographic record
Abstract
Primates exhibit more diversity in locomotor behavior than any other mammalian order. Discrete locomotor adaptations are characterized by distinct patterns of phalangeal orientation with dissimilar patterns of mechanical loading. Phalangeal shaft length and curvature are frequently used to infer locomotion in fossil anthropoids given the direct and repetitive contact between the hand and substrate; however, most fossil anthropoid phalanges are fragmentary or incomplete. Given that articular epiphyses and associated collateral ligaments are subject to the same locomotor behaviour‐specific mechanical loading that influences longitudinal shaft curvature and length, it is reasonable to predict that the proximal and distal articular surface morphology and collateral ligament fossae (CLF) are also influenced, at least in part, by locomotion. We test the hypothesis that anthropoid phalangeal articular and collateral ligament fossae (CLF) morphology (i.e. contour curvature, size) varies with respect to locomotion and that more terrestrial primates experiencing higher compressive loads will have larger CLF's and increased mediolateral articular contour curvatures to resist mediolateral joint displacement. Segmented 3D models of proximal and distal articular surfaces and CLF's were obtained for the proximal phalanges (digits 2–5) of 16 locomotor diverse anthropoid genera (n=234 individuals). Direct comparisons among individual taxa and locomotor groupings reveal that articular and CLF morphology is a composite function of size, phylogeny, and locomotion but that more terrestrial primates have greater mediolateral articular contour curvatures and larger, deeper CLF's relative to more arboreal primates who have less mediolaterally restrictive joints with increased dorsoventral curvatures and smaller CLF's.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".