Bioinformatics and Human Identification in Mass Fatality Incidents: The World Trade Center Disaster*
Bibliographic record
Abstract
Victim identification initiatives undertaken in the wake of Mass Fatality Incidents (MFIs) where high-body fragmentation has been sustained are often dependent on DNA typing technologies to complete their mandate. The success of these endeavors is linked to the choice of DNA typing methods and the bioinformatic tools required to make the necessary associations. Several bioinformatic tools were developed to assist with the identification of the victims of the World Trade Center attacks, one of the most complex incidents to date. This report describes one of these tools, the Mass Disaster Kinship Analysis Program (MDKAP), a pair-wise comparison software designed to handle large numbers of complete or partial Short Tandem Repeats (STR) genotypes, and infer identity of, or biological relationships between tested samples. The software performs all functions required to take full advantage of the information content of processed genotypic data sets from large-scale MFIs, including the collapse of victims data sets, remains re-association, virtual genotype generation through gap-filling, parentage trio searching, and a consistency check of reported/inferred biological relationships within families. Although very few WTC victims were genetically related, the software can detect parentage trios from within a victim's genotype data set through a nontriangulated approach that screens all possible parentage trios. All software-inferred relationships from WTC data were confirmed by independent statistical analysis. With a 13 STR loci complement, a fortuitous parentage trio (FPT) involving nonrelated individuals was detected. Additional STR loci would be required to reduce the risk of an FPT going undetected in large-scale MFIs involving related individuals among the victims. Kinship analysis has proven successful in this incident but its continued success in larger scale MFIs is contingent on the use of a sufficient number of STR loci to reduce the risk of undetected FPTs, the use of mtDNA and Y-STRs to confirm parentage and of bioinformatics that can support large-scale comparative genotyping schemes capable of detecting parentage trios from within a group of related victims.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".