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Record W197177183 · doi:10.5555/2048476.2048490

Interfacing DEVS and visualization models for emergency management

2011· article· en· W197177183 on OpenAlex
Mohammad Moallemi, Shafagh Jafer, Ahmed Sayed Ahmed, Gabriel Wainer

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
Typearticle
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsDEVSInterfacingVisualizationComputer scienceSimulationDistributed computingReal-time computingHuman–computer interactionModeling and simulationArtificial intelligence

Abstract

fetched live from OpenAlex

We introduce a method to integrate Cell-DEVS models with DEVS-based robotic agents and an advanced Immersive environment for Emergency Management. The emergency is handled by an autonomous robot controlled by a real-time DEVS model. The model controlling the robot interacts with a simulation for emergencies, receiving realtime data about its location on a cell space. The immersive environment is used to visualize the emergency and its management. The simulation results of both the cell-DEVS emergency model and the DEVS-based robotic first responder are visualized dynamically at real-time. The goal is to show how to integrate cellular modeling in a real-time platform and the DEVS formal framework as a collaboration mechanism. The real-time visualization allows for supervisory control of the emergency and first responders activities. 1.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.662

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)0.0010.000

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 head0.394
GPT teacher head0.470
Teacher spread0.076 · 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

Citations8
Published2011
Admission routes1
Has abstractyes

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