Expert Testimony and Positive Identification of Human Remains Through Cranial Suture Patterns
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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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".