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Record W2007638796 · doi:10.1117/12.817830

C2SM: a mobile system for detecting and 3D mapping of chemical, radiological, and nuclear contamination

2009· article· en· W2007638796 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 · 2009
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadiation Detection and Scintillator Technologies
Canadian institutionsRoyal Canadian Mounted PoliceGolder Associates (Canada)
Fundersnot available
KeywordsComputer scienceSituation awarenessMobile robotRobotComputer visionRadiological weaponArtificial intelligenceImage resolutionRemote sensingDetectorReal-time computingGeologyAerospace engineering

Abstract

fetched live from OpenAlex

CBRN Crime Scene Modeler (C2SM) is a prototype mobile CBRN mapping system for First Responders in events where Chemical, Biological, Radiological and Nuclear agents where used. The prototype operates on board a small robotic platform, increases situational awareness of the robot operator by providing geo-located images and data, and current robot location. The sensor suite includes stereo and high resolution cameras, a long wave infra red (thermal) camera and gamma and chemical detectors. The system collects and sends geo-located data to a remote command post in near real-time and automatically creates 3D photorealistic model augmented with CBRN measurements. Two prototypes have been successfully tested in field trials and a fully ruggedised commercial version is expected in 2010.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0130.004

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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 designBench or experimental
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

Citations19
Published2009
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