Serious Gaming for Improvised Explosive Device Neutralization Training
Bibliographic record
Abstract
An improvised explosive device (IED) is a “homemade” bomb intended to cause great harm when it explodes. The public safety task of identifying and neutralizing IEDs falls to military and police services often called explosive disposal units (EDU) who act to neutralize the threat associated with the IED either rendering it inoperable or destroying it safely. EDUs train in various aspects of explosive handling and investigation but are limited in the tools available for safely analyzing real world bombs. This paper describes a game based approach to IED training that employs an interactive 3D simulation to spatially identify key IED components of interest. We give an example of how this approach might be used and provide a preliminary evaluation of its potential effectiveness. We employ images formed from a Digital Imaging and Communications in Medicine (DICOM) system captured using Magnetic Resonance Imaging (MRI) technology to a virtual IED in a game. Empirical evaluation and EDU testimony suggest accurate representation of the IED and the potential efficacy of the proposed approach for successfully identifying components in the bomb for the purposes of EDU training.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".