A Metric Method for Sex Determination Using the Proximal Femur and Fragmentary Hipbone*<sup>,†</sup>
Bibliographic record
Abstract
The pubic bone is considered one of the best sources of information for determining sex using skeletal remains, but can be easily damaged postmortem. This problem has led to the development of nonpelvic methods for cases when the pubic bone is too damaged for analysis. We approached this problem from a different perspective. In this article, we present an approach using new measurements and angles of the proximal femur to recreate the variation in the pubic bone. With a sample from the Terry Collection (n > 300), we use these new variables along with other traditional measurements of the femur and hipbone to develop two logistic regression equations (femur and fragmentary hipbone, and femur only) that are not population specific. Tests on an independent sample (Grant Collection; n = 37-40) with a different pattern of sexual dimorphism resulted in an allocation accuracy of 95-97% with minimal difference by sex.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".