Evaluating the Accuracy and Precision of Cranial Morphological Traits for Sex Determination
Bibliographic record
Abstract
Sex determination is a key analysis that forensic anthropologists perform in order to construct a biological profile of human remains. The techniques used in forensic investigations must meet the Mohan or Daubert criteria, for admissibility in a court of law. In this study, the precision and accuracy of 21 morphological characteristics of the skull were tested on a modern sample of 50 adult crania of European White ancestry. The following craniofacial features are identified as high-quality traits, defined by intraobserver error <or=10% and accuracy >or=80%: mastoid size, supraorbital ridge size, general size and architecture, rugosity of the zygomatic extension, size and shape of the nasal aperture, and gonial angle. Ninety-six percent accuracy and 92% precision were achieved using 20 traits in combination. Fisher's exact probability tests revealed no significant differences (p=0.05) in the levels of precision or accuracy between age categories. Sex-related bias in accuracy was found for the following cranial features: ramus symphysis (p=0.009), zygomatic extension (p=0.0016), and occipital markings (p=0.0013). These traits demonstrated a greater tendency to be scored male than female.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".