Positive Personal Identification of Human Remains Based on Thoracic VertebralMargin Morphology
Bibliographic record
Abstract
Radiography has long been used by anthropologists to establish positive personal identification of human remains in forensic cases. These methods have been largely ad hoc and depend upon specific congenital or pathological bone markers. Court rulings, such as Daubert and Mohan have, however, pushed the discipline toward more statistically supportable methods of identification. This study describes the use of normal morphological variation of the thoracic vertebrae to identify human remains. Radiographs from healthy, male individuals, aged 18-55 were examined to identify normally varying features of vertebral morphology. The frequency of occurrence of these features was calculated, tested, and found to be stable in the given sample. The frequencies were compared to establish which sets of traits varied independently of one another. Finally, unknown radiographs were compared to known samples to test the applicability of this method in determining positive identification, with 21 of 24 (87.5%) unknown radiographs positively identified.
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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 | 0.001 | 0.029 |
| 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".