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Record W2047779487 · doi:10.1117/12.783067

3D modeling of environments contaminated with chemical, biological, radiological and nuclear (CBRN) agents

2008· article· en· W2047779487 on OpenAlexaff
Piotr Jasiobedzki, Ho-Kong Ng, Michel Bondy, Carl H. McDiarmid

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsRoyal Canadian Mounted Police
Fundersnot available
KeywordsComputer scienceRadiological weaponDetectorRadiationRadiation monitoringImage resolutionRemote sensingComputer visionArtificial intelligenceOpticsPhysicsGeologyRadiochemistry

Abstract

fetched live from OpenAlex

CBRN Crime Scene Modeler (C2SM) is a prototype 3D modeling system for first responders investigating environments contaminated with Chemical, Biological, Radiological and Nuclear agents. The prototype operates on board a small robotic platform or a hand-held device. The sensor suite includes stereo and high resolution cameras, a long wave infra red camera, chemical detector, and two gamma detectors (directional and non-directional). C2SM has been recently tested in field trials where it was teleoperated within an indoor environment with gamma radiation sources present. The system has successfully created multi-modal 3D models (geometry, colour, IR and gamma radiation), correctly identified location of radiation sources and provided high resolution images of these sources.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.015
GPT teacher head0.205
Teacher spread0.190 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2008
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicRadiation Detection and Scintillator TechnologiesFrench-language works237,207