Bibliographic record
Abstract
Since the earliest descriptions of the pubis length measurement, it has been recognized that the location of the key landmark in the acetabulum has to be estimated. Using samples from the Terry Collection (n = 324) and the Coimbra Collection (n = 232), the purpose of this research is to, first, test the reproducibility of a new alternative to the traditional measurement of the pubis, and second, to use the best measurement of the pubis along with other measurements of the hipbone and femur to develop a metric method that can be used with confidence to determine the sex of individuals of various geographic origins and time periods. In this study, it was found that, first, the alternative pubis measurement, known as the superior pubis ramus length (SPRL), can be collected more reliably with less mean intra-observer error (0.57%) than the more commonly used manner of measuring the pubis (2.7%). Second, a logistic regression sex determination method using the SPRL, along with other measurements of the hipbone and femur, has an allocation accuracy of 90% to 98.5% (depending on the model used and the manner of testing) across independent samples. Third, traditional racial categorization was irrelevant to the accuracy of the method. Fourth, measurement error greater than 2% in the measurement of the pubis can be the difference between a correct and an incorrect allocation of sex, particularly in borderline cases.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".