Evaluation of rapid DNA using ANDE™ in a technical exploitation Level 2 laboratory workflow
Bibliographic record
Abstract
A trial of rapid DNA (rDNA), a fully automated DNA profiling system, within a technical exploitation (TE) workflow is an important endeavor. In the 2019 Ardent Defender (AD) exercise, the Deployable Technical Analysis Laboratory (DTAL), of the Canadian Department of National Defence (DND), evaluated the use of rDNA using ANDE™. Sixteen samples were processed during a pre-exercise "controlled" setting, 44 samples were from an "uncontrolled" environment during the exercise, and 22 samples were buccal swabs. The proportion of profiles suitable for upload to ANDE™ was 95.5% of buccal samples (21/22), 66.7% controlled samples, and 15.9% for uncontrolled samples. A considerable difference was observed in the proportions of complete DNA profiles obtained from all exploited items between the controlled (58.3%) and uncontrolled (15.9%) trials and in the proportions of samples where no DNA was detected (16.7% controlled trial vs. 56.8% uncontrolled trial). Overall, the trials highlighted the potential to gain identity intelligence using rDNA within a TE workflow and revealed the impact of operational constraints and the need to improve certain TE practices to gain the most benefit from rDNA. It also demonstrated the benefit of including an uncontrolled component for a more realistic indication of rDNA effectiveness in operational settings and highlighted operational practices impacting rDNA success. Mixture deconvolution was difficult as current guidelines do not consider some of the stochastic effects produced by the rDNA analysis; however, overall, the study demonstrated that rDNA using the ANDE™ instrument could be successfully incorporated into a TE workflow within a deployable laboratory.
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How this classification was reachedexpand
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 | 0.004 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".