Trace <scp>DNA</scp> Sampling Success from Evidence Items Commonly Encountered in Forensic Casework
Bibliographic record
Abstract
Trace DNA analysis is a significant part of a forensic laboratory's workload. Knowing optimal sampling strategies and item success rates for particular item types can assist in evidence selection and examination processes and shorten turnaround times. In this study, forensic short tandem repeat (STR) casework results were reviewed to determine how often STR profiles suitable for comparison were obtained from "handler" and "wearer" areas of 764 items commonly submitted for examination. One hundred and fifty-five (155) items obtained from volunteers were also sampled. Items were analyzed for best sampling location and strategy. For casework items, headwear and gloves provided the highest success rates. Experimentally, eyeglasses and earphones, T-shirts, fabric gloves and watches provided the highest success rates. Eyeglasses and latex gloves provided optimal results if the entire surfaces were swabbed. In general, at least 10%, and up to 88% of all trace DNA analyses resulted in suitable STR profiles for comparison.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".