Forensic DNA Phenotyping: Examining knowledge and operational view from police officers
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.
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.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".