The First Successful Use of a Low Stringency Familial Match in a French Criminal Investigation
Bibliographic record
Abstract
We describe how a very simple application of familial searching resolved a decade-old, high-profile rape/murder in France. This was the first use of familial searching in a criminal case using the French STR DNA database, which contains approximately 1,800,000 profiles. When an unknown forensic profile (18 loci) was searched against the French arrestee/offender database using CODIS configured for a low stringency search, a single low stringency match was identified. This profile was attributed to the father of the man suspected to be the source of the semen recovered from the murder victim Elodie Kulik. The identification was confirmed using Y-chromosome DNA from the putative father, an STR profile from the mother, and finally a tissue sample from the exhumed body of the man who left the semen. Because of this identification, the investigators are now pursuing possible co-conspirators.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".