Forensic DNA Phenotyping: Examining knowledge and operational view from police officers
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
Forensic DNA phenotyping (FDP) is a tool predicting physical characteristics from DNA to provide investigative leads. Research has mainly focused on the development and validation of molecular marker panels and associated statistical models to predict phenotypes. However, little is known about the operational value of DNA phenotyping, as perceived by the targeted users (i.e. police officers involved in criminal investigations). We used a questionnaire to survey 163 officers across Québec (Canada), and who are involved in major crime investigations, to better understand their knowledge and opinion regarding DNA phenotyping. Their responses show that a majority (63 %) are not yet familiar with DNA phenotyping. However, most respondents (58 %) support its use, especially for crimes against the person, if proven reliable. This research emphasizes the relevance of surveying police officers during the development and implementation of such operational forensic tools, as their expectations were not entirely in line with the current and anticipated possibilities of phenotyping, particularly with regard to the most useful traits to target. Respondents consider most useful predictions on eye colour, ethnicity, age and height, whereas it is biogeographical origin that is currently predicted (even if not a phenotype), and the last two traits are difficult to accurately predict. The perspective of police officers gathered here also argues in favor of involving other actors of the justice system to better delineate the scope of FDP in criminal cases and to improve its integration throughout the judicial process.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it