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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 OpenAlexaff
Mariano Zapatero, Rodrigo Castro, Gabriel Wainer, Maysoun Houssein

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.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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

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

Citations5
Published2011
Admission routes1
Has abstractyes

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