Genetic Genealogy and its Use in Criminal Investigations: Are We Heading Towards a Universal Genetic Database?
Bibliographic record
Abstract
In April 2018, Joseph DeAngelo also known as The Golden State Killer was caught and convicted. This was made possible by 40-year-old DNA evidence, genetic genealogy, and current information systems technology. This paper will discuss the history of genetic information such as DNA testing used in forensics, and consider information technologies effect on the future of criminal investigations. The main focus is genetic databases and their management. How will the management of these databases affect the public and law enforcement? Could a universal genetic database create solutions to the current criminal database systems, often critiqued for being discriminatory? How can we use genetic genealogy more efficiently to solve crimes? The sources used for this exploration include companies such as GEDmatch, 23andME, and Ancestry; key players of the field such as Barbara Rae Venter and CeCe Moore; newspaper articles, statistics, and academic journals.
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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 | 0.028 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.014 | 0.032 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".