Ethical Issues in Secondary Uses of Human Biological Materials from Mass Disasters
Bibliographic record
Abstract
In the trauma surrounding mass disasters, the need to identify victims accurately and as soon as possible is critical. DNA identification testing is increasingly used to identify human bodies and remains where the deceased cannot be identified by traditional means. This form of testing compares DNA taken from the body of the deceased with DNA taken from their personal items (e.g. hairbrush, toothbrush etc.) or from close biological relatives. DNA identification testing was used to identify the victims of the terrorist attack on the World Trade Center in New York on September 11, 2001, and of the victims of the Tsunami that hit Asia on December 26, 2004. Shortly after the 9/11 attack, police investigators asked the victims' families for personal items belonging to the missing, and for DNA samples from family members themselves. The New York medical examiner's office coordinated the DNA identification testing program; however, some of the identification work was contracted out to private laboratories.
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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 | 0.277 | 0.262 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.030 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.024 | 0.020 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".