Explosive detection canines in the field: a multi-site black box validation study
Bibliographic record
Abstract
In 2009, the National Research Council called upon the forensic science community to standardize the best practices and guidelines in the collection and analysis of evidence with the goal of ensuring quality and consistency within the field. In response to this need, the Organization of Scientific Area Committees for Forensic Science (OSAC) was established to coordinate the development of best practices and standards in the forensic sciences. The OSAC Dogs and Sensors subcommittee was part of this initiative focusing on standardizing training and certification protocols for canine detection teams. Though efforts to create and promote such standards are ongoing worldwide, the developed assessments for both training and operational contexts have yet to be empirically validated. As a first step toward addressing this gap, a proof-of-concept black box study was carried out to assess the OSAC explosive canine detection standard based on performance of explosive detection canines. The evaluations were held in three separate geographic locations with a total of 56 canine/handler teams, took place over 2 days, and included searches recommended within the ANSI/ASB Standard 092 as well as scenarios designed to more closely mimic what the teams might experience in practice. Overall, the results from the individual canine/handler team responses revealed that no team would have passed the OSAC certification; however, the results indicated comparable performance on both assessment types (standard assessments and operational scenarios). Additionally, canine/handler performance varied significantly across all three trials in both correct alert, false alert rates, and detection success rate across the mandatory six different explosive types presented. These findings suggest that the performance on Standard 092 certification assessments may predict operational effectiveness. The results also suggest that the variation in performance is attributable to the diversity of training aid material routinely available to the participating teams.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".