Postcranial Sex Estimation of Individuals Considered Hispanic
Bibliographic record
Abstract
When forensic anthropologists estimate the sex of Hispanic skeletal remains using nonpopulation specific metric methods, initial observations cause males to frequently misclassify as female. To help improve these methods, this research uses postcranial measurements from United States-Mexico border migrant fatalities at the Pima County Office of the Medical Examiner in Tucson, Arizona, as well as Hispanic individuals from the Forensic Anthropology Data Bank. Using a total of 114 males and 28 females, sectioning points and discriminant functions provide classification rates as high as 89.43% for Hispanic individuals. A test sample assessed the reliability of these techniques resulting in accuracy up to 99.65%. The clavicle maximum length measurement provides the best univariate estimate of sex, while the radius provides the best multivariate estimated of sex. The results of this research highlight the need for population specific data in the creation of a biological profile, especially when working with individuals considered Hispanic.
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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 | 0.001 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".