Genetic Genealogy and its Use in Criminal Investigations: Are We Heading Towards a Universal Genetic Database?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it