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Record W2156845725 · doi:10.5555/2431518.2431635

Architecture for integrated modeling, simulation and visualization of environmental systems using GIS and cell-devs

2011· article· en· W2156845725 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

VenueWinter Simulation Conference · 2011
Typearticle
Languageen
FieldComputer Science
TopicDistributed and Parallel Computing Systems
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceVisualizationWorkflowGeographic information systemDistributed GISFlexibility (engineering)Frame (networking)Information systemArchitectureData visualizationDEVSData scienceSystems engineeringAM/FM/GISDatabaseGIS applicationsModeling and simulationData miningEngineeringSimulationGeographyRemote sensing

Abstract

fetched live from OpenAlex

Online Geographic Information Systems (GIS) and their associated data visualization technologies are playing an increasingly important role in providing updated information for environmental models. The analysis of simulation results are often benefited from their georeferenced animated visualization. We present an architectural web-based integration of the DCD++ distributed modeling and simulation frame-work as the centerpiece of a GIS-based scientific workflow to study environmental phenomena. We demonstrate an end-to-end application of the proposed architecture by means of a wildfire spreading model, backed by online updates of different parameters affecting the environmental system under study. Google Earth and GRASS are the two GIS systems selected to highlight the flexibility of the integrated system.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.725
Threshold uncertainty score0.514

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 head0.064
GPT teacher head0.282
Teacher spread0.218 · 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