An Instrument for Automated Purification of Nucleic Acids from Contaminated Forensic Samples
Bibliographic record
Abstract
Forensic crime scene sample analysis, by its nature, often deals with samples in which there are low amounts of nucleic acids, on substrates that often lead to inhibition of subsequent enzymatic reactions such as PCR amplification for STR profiling. Common substrates include denim from blue jeans, which yields indigo dye as a PCR inhibitor, and soil, which yields humic substances as inhibitors. These inhibitors frequently co-extract with nucleic acids in standard column or bead-based preps, leading to frequent failure of STR profiling. We present a novel instrument for DNA purification of forensic samples that is capable of highly effective concentration of nucleic acids from soil particulates, fabric, and other complex samples including solid components. The novel concentration process, known as SCODA, is inherently selective for long charged polymers such as DNA, and therefore is able to effectively reject known contaminants. We present an automated sample preparation instrument based on this process, and preliminary results based on mock forensic samples.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".