A revised method of sexing the human innominate using Phenice's nonmetric traits and statistical methods
Bibliographic record
Abstract
The traits of the pubis described by Phenice (Am J Phys Anthropol 30 (1969) 297-302) have been used extensively by physical anthropologist for sex estimation. This study investigates all three of Phenice's characteristics in an approach similar to Walker's (Am J Phys Anthropol 136 (2008) 39-50) study using observations from the cranium and mandible. The ventral arc, the subpubic contour, and the medial aspect of the ischio-pubic ramus were scored on a five-point ordinal scale from a sample of 310 adult, left innominates of known ancestry and sex from the Hamann-Todd Human Osteological Collection and the W.M. Bass Donated Skeletal Collection. Four observers with varying levels of experience blindly scored each trait using new descriptions and illustrations adapted from those originally created by Phenice. The scores were then analyzed with ordinal logistic regression. Using all three traits for sex classification, the mean correct classification rate was 94.5% cross-validated for experienced observers. Intra- and interobserver error in trait scoring was low for all three traits and agreement levels ranged from moderate to substantial. Tests of the method on an independent validation sample provided a classification accuracy of 86.2%. This revision of the Phenice (Am J Phys Anthropol 30 (1969) 297-302) technique is a reliable and valid method of sex estimation from the human innominate that meets the Daubert criteria for court admissibility.
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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 | 0.031 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".