Sexual dimorphism and allometry in human scapular morphology
Bibliographic record
Abstract
Abstract Scapular morphology is highly variable across the human population and appears to be sexually dimorphic – differing significantly between males and females. However, previous investigations of sexual dimorphism in scapula shape have not considered the effects of allometry (the relationship between size and shape). Disentangling allometry from sexual dimorphism is necessary because apparent sex-based differences in morphology could be due to inherent differences in body size. This study aimed to investigate sexual dimorphism in scapula shape and examine the role of allometry in sex-based variation. We used three-dimensional geometric morphometrics with Procrustes ANOVA to quantify scapula shape variation associated with sex and size in 125 scapulae. Scapular morphology significantly differed between males and females, and males tended to have larger scapulae than females for the same body height. We found that males and females exhibited distinct allometric relationships, and sexually dimorphic shape changes did not align with male- or female-specific allometry. A secondary test revealed that sexual dimorphism in scapula shape persisted between males and females of similar body heights. Overall, our findings indicate that sex-based differences in scapular shape are independent of size-shape relationships. Our results shed light on the potential role of sexual selection in human shoulder evolution, present new hypotheses for biomechanical differences in shoulder function between sexes, and identify relevant traits for improving sex classification accuracy in forensic analyses.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".