Enhanced Kinship Analysis and STR-based DNA Typing for Human Identification in Mass Fatality Incidents: The Swissair Flight 111 Disaster
Bibliographic record
Abstract
A bioinformatic tool was developed to assist with the victim identification initiative that followed the Swissair Flight 111 disaster. Making use of short tandem repeat (STR) DNA typing data generated with AmpFlSTR Profiler Plus (PP) and AmpFlSTR COfiler(CO) kits, the software systematically compared each available STR genotype with every other genotype. The matching algorithm was based on the search for: (i) direct matches to genotypes derived from personal effects; and (ii) potential kinship associations between victims and next-of-kin, as measured by allele sharing at individual loci. The software greatly assisted parentage analysis by enabling kinship evaluation in situations where complete parentage trios were unavailable and, in some situations, with distantly related relatives. Exclusion of fortuitous kinship associations (FKA) was made possible through the recovery at the disaster site of at least one remains for every sought-after victim, and was incorporated into the kinship software. The data from the 13 combined STR loci produced 6 and 23 times fewer FKAs when compared with PP alone and AmpFlSTR Profiler (PR) alone, respectively. Identification leads or confirmations of identification were obtained for 218 victims for which DNA reference samples (personal effects and kin) had been submitted. Confirmation of an inferred kinship association was sought through frequency and likelihood calculations, as well as corroborative data from other identification modalities. The use of a simple, yet powerful, automated genotype comparison approach and the use of megaplexes with high power of discrimination (PD) values extended considerably the identification capabilities in the case of the Swissair disaster. The DNA typing identification modality proved to be a valuable component of the large arsenal of identification tools deployed in the aftermath of this disaster.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".