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Record W4247908489 · doi:10.32920/ryerson.14652726.v1

Game-based threat assessment tool for improvised explosive device neutralization training

2021· preprint· en· W4247908489 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Data Processing Techniques
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAcronymExplosive materialComputer scienceKey (lock)Computer securityHuman–computer interactionRisk analysis (engineering)SimulationMedicine

Abstract

fetched live from OpenAlex

CBRNe is an acronym referring to chemical, biological, radiological, nuclear and explosives. When specialized response teams deal with CBRNe-related incidents, one of the guiding principles is to avoid contact with the threat until the nature of the threat can be determined. Our research demonstrates that we can safely create, inspect and manipulate a 3D model of a suspected CBRNe threat within a physics-based game engine where models are created from extremely accurate data gathered from Magnetic Resonance Imaging (MRI) sensors. Our system is able to provide first responders the ability to visually identify key IED components of interest and obtain relevant information directly from the simulation. The primary goal of our research is to demonstrate that the functionality we developed can be used to provide accurate information to its users for the purposes of training and potentially assist CBRNe planning efforts in the future.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.411
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.053
GPT teacher head0.332
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations1
Published2021
Admission routes1
Has abstractyes

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