Metric pair-matching of calcanei in cases of commingled human remains: examining 21st century black, white, and Coloured South African populations
Bibliographic record
Abstract
This research aimed to establish an accurate method of osteometric pair-matching calcanei in White, Black, and Coloured South Africans. Paired calcanei of 419 adult individuals (210 males, 209 females) were utilized. Both the left and right calcanei of 139 White South Africans (70 males, 69 females), 140 Black South Africans (70 males, 70 females), and 140 Coloured South Africans (70 males, 70 females) were examined. Six measurements were collected from each calcaneus. The variables that exhibited the least amount of directional and absolute asymmetry were maximum length (MAXL) and middle breadth (MIDB). The variables that exhibited greater degrees of directional and absolute asymmetry were articular facet dimensions (dorsal articular facet length (DAFL), dorsal articular facet breadth (DAFB), middle articular facet length (MAFL), and middle articular facet breadth (MAFB)). There were no statistically significant differences in directional and absolute asymmetry between sexes for most variables. There were statistically significant differences in absolute asymmetry between the three South African populations for some variables. Therefore, population-specific osteometric pair-matching methods are necessary. The statistic M was utilized to create reference tables for osteometric pair-matching. The values of M for MAXL for pair-matching comparisons resulted in the greatest reduction in the number of possible pairs with acceptable false rejection rates. The osteometric pair-matching tables from the current study can be utilized, in combination with visual pair-matching techniques, to assist in the resolution of commingled remains 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".