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Record W2030750759 · doi:10.1145/1643928.1643980

Crime scene robot and sensor simulation

2009· article· en· W2030750759 on OpenAlexafffund
Robert Codd-Downey, Michael Jenkin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaDefence Research and Development Canada
KeywordsSuiteVirtual realityComputer scienceRobotSoftwareConstruct (python library)SimulationTraining systemPhysics engineSimulation softwareSimulation trainingTraining (meteorology)Human–computer interactionArtificial intelligenceOperating system

Abstract

fetched live from OpenAlex

Virtual reality has been proposed as a training regime for a large number of tasks from surgery rehearsal (cf. [Robb et al. 1996], to combat simulation (cf. [U. S. Congress, Office of Technology Assessment 1994]) to assiting in basic design (cf. [Fa et al. 1992]). Virtual reality provides a novel and effective training medium for applications in which training "in the real world" is dangerous or expensive. Here we describe the C2SM simulator system -- a virtual reality-based training system that provides an accurate simulation of the CBRNE Crime Scene Modeller System (see [Topol et al. 2008]). The training system provides a simulation of both the underlying robotic platform and the C2SM sensor suite, and allows training of the system to take place without physically deploying the robot or the simulation of chemical and radiological agents that might be present. This paper describes the basic structure of the C2SM simulator and the software components that were used to construct it.

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 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 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 score0.936
Threshold uncertainty score0.221

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 head0.012
GPT teacher head0.226
Teacher spread0.215 · 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 teacher head, 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
Published2009
Admission routes2
Has abstractyes

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