Forensic Odontology Radiography and Imaging in Disaster Victim Identification Positional statement of the members of the Disaster Victim Identification working group of the International Society of Forensic Radiology and Imaging
Bibliographic record
Abstract
The use of radiography by forensic odontologists for the purposes of disaster victim identification (DVI)\nwas established in 1949, when it was used to assist in the identification of the victims of the Great\nLakes liner “Noronic” disaster in Toronto, Canada. Of the 119 victims of the disaster, positive\nidentification matches were established for 24 of the most severely disfigured cases through the use\nof comparative odontology radiography (1-3). Today radiography is an established tool of forensic\nodontologists for DVI. The precise requirements for dental radiography for any given mass fatality\nincident will be determined by the working practices of the forensic odontologists engaged in the\ninvestigation.
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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 | 0.000 | 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 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".