Expert Testimony and Positive Identification of Human Remains Through Cranial Suture Patterns
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
North American forensic anthropological research should conform to the Daubert criteria (U.S.A.) and Mohan ruling (Canada) to ensure admissibility in a court of law. Positive identification through radiographic comparison of antemortem and postmortem cranial suture patterns was evaluated in light of these criteria. The technique is based on reliable principles, but problems with terminology and the resolution of radiographs make Sekharan's method difficult to apply. Using the location, length, and slope of a suture's component lines, rather than Sekharan's descriptions of sutural configurations, it is possible to determine the probability of a particular suture pattern occurring in more than one individual. A match of four consecutive lines is sufficient to establish positive identification. This approach meets the Daubert and Mohan criteria, although resolution of radiographs is still a major limitation. Computed tomography (CT) scans may prove a more useful modality for positive identification, due to better resolution and greater availability.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it