Dense single nucleotide polymorphism testing revolutionizes scope and degree of certainty for source attribution in forensic investigations
Bibliographic record
Abstract
The field of forensic DNA analysis has experienced significant advancements over the years, such as the advent of DNA fingerprinting, the introduction of the polymerase chain reaction for increased sensitivity, the shift to a primary genetic marker system based on short tandem repeats, and implementation of national DNA databases. Now, the forensics field is poised for another revolution with the advent of dense single nucleotide polymorphisms (SNPs) testing. SNP testing holds the potential to significantly enhance source attribution in forensic cases, particularly those involving low-quantity or low-quality samples. When coupled with genetic genealogy and kinship analysis, it can resolve countless active cases as well as cold cases and cases of unidentified human remains, which are hindered by the limitations of existing forensic capabilities that fail to generate viable investigative leads with DNA. The field of forensic genetic genealogy combined with genome-wide sequencing can associate relatives as distant as the seventh-degree and beyond. By leveraging volunteer-populated databases to locate near and distant relatives, genetic genealogy can effectively narrow the candidates linked to crime scene evidence or aid in determining the identity of human remains. With decreasing DNA sequencing costs and improving sensitivity of detection, forensic genetic genealogy is expanding its capabilities to generate investigative leads from a wide range of biological evidence.
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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 | 0.024 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".