Morphological Assessment of Ancestry using Cranial Macromorphoscopics,
Bibliographic record
Abstract
Ancestry estimation is essential for biological profile estimation in forensic anthropology. Hefner (2009) and Osteoware (Smithsonian Institution, 2011) presented 16 macromorphoscopic traits that can be scored for standardized data collection and can also be used within a statistical framework to estimate ancestry. The primary purpose of this research was to examine the utility of these traits for assessing ancestry. Tests of observer agreement and the range of variation in trait expression were evaluated. A sample of 208 American whites and blacks from the Hamann-Todd Collection were scored, and several classification methods were utilized in accordance with Hefner (2009). Correct classifications for the pooled sex analyses ranged from 73.3% to 86.6% and from 46.7% to 64.3% when the sexes were analyzed independently. Interobserver agreement was variable and was found to be lower than that presented in Hefner (2009). Trait expression was variable in both groups and was generally consistent with Hefner's findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.031 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".