The Gander Disaster: Dental Identification in a Military Tragedy
Bibliographic record
Abstract
The authors record the contributions of dentistry to the identification of victims of one of the most significant disasters in aviation and U.S. military history--the December 1985 crash of a DC-8 charter airliner near Gander, Newfoundland (now known as Newfoundland and Labrador), Canada, which killed 248 Army personnel and 8 crewmembers. Most of the dental records of the military victims were destroyed in the crash, and, as a result, this loss hampered dental identification. Nevertheless, dental identification was the primary means of identification for many because a very high percentage of the bodies were severely burned and fragmented. Many phases of the U.S. identification efforts have been reported, but the dental-investigation aspects have been mentioned only in passing. Therefore, this article documents the dental team's organization, methodology, and a variety of remarkable problems that the team encountered.
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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 | 0.021 |
| 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".