Quantifying Sexual Dimorphism in Scapular Morphology
Bibliographic record
Abstract
This project explores sexual differences in scapular morphology through linear measurements (LMs) and geometric morphometric (GM) methods. Traditional LMs assess size while GM methods primarily assess shape. Our two hypotheses are as follows: the human scapula expresses sexual dimorphism in both size and shape, and given that shape differences exist, this bone will producehigher correct classification rates when assessed for sex using GM rather than using LMs. Three-dimensional data were obtained from the UTK Donated Skeletal Collection (n = 106); linear data were obtained from the Forensic Anthropology Data Bank (n = 1,252). We modeled these data separately to quantify levels of sexual dimorphism in size (LM) and shape (GM) variability. Linear measurements correctly identified 93% of the sample; the GM method could only correctly identify ~70% of the sample when following the GM protocol for the scapular landmark collection outlined in Uhl et al. (2007). However, GM data produced correct classification rates of over 93% when checked for correlation of centroid size. These results indicate that both size and shape drive differences between female and male shoulder girdle morphology, though the primary contributor is sexual size dimorphism. Size-free and/or allometric differences were also noted within this sample but with a much smaller impact on morphology. The human scapula produces high accuracy rates for sex estimation, regardless of whether LMs or GMs are utilized.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| 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 | 0.001 |
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".