Secondary DNA transfer on denim using a human blood analogue
Bibliographic record
Abstract
Abstract DNA quantification technology has increased in accuracy and sensitivity, now allowing for detection and profiling of trace DNA. Secondary DNA transfer occurs when DNA is deposited via an intermediary source (e.g. clothing, tools, utensils). Multiple courtrooms have now seen secondary transfer introduced as an explanation for DNA being present at a crime scene, but sparse experimental studies mean expert opinions are often limited. Here, we used bovine blood and indigo denim substrates to quantify the amount of secondary DNA transfer and quality of STRs under three different physical contact scenarios: passive, pressure, and friction. We showed that the DNA transfer was highest under a friction scenario, followed by pressure and passive treatments. The STR profiles showed a similar, albeit less pronounced trend, with correctly scored alleles and genotype completeness being highest under a friction scenario, followed by pressure and passive. DNA on the primary substrate showed a decrease in concentration and genotype completeness both immediately and at 24 hours, suggestive of a loss of DNA during the primary transfer. The majority of secondary transfer samples amplified less than 50% of STR loci regardless of contact type. This study showed that while DNA transfer is common between denim, this is not manifested in full STR profiles. We discuss the possible technical solutions to partial profiles from trace DNA, and more broadly the ubiquity of secondary DNA transfer.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".