MétaCan
Menu
Back to cohort
Record W4362576762 · doi:10.22215/etd/2023-15447

A Web Based Architecture to Operationalize Geospatial Simulation Environments

2023· dissertation· en· W4362576762 on OpenAlexaff
Bruno St‐Aubin

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldDecision Sciences
TopicSimulation Techniques and Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsDEVSComputer scienceGeospatial analysisSoftware engineeringOperationalizationModular designSystems engineeringModeling and simulationSimulationEngineeringProgramming language

Abstract

fetched live from OpenAlex

Simulation is inherently multi-disciplinary. It requires knowledge about the system under study, expertise in simulation theory to define models and programming skills to implement models. Geospatial simulation requires an additional layer of expertise in topology, geospatial data structures, spatial analysis, computational geometry, and other related topics. Commercial modeling and simulation software can be used to provide an environment to facilitate simulation studies for users. However, these software tend to be narrowly scoped to specific business applications and tightly couple model and simulator. As such, it is difficult to expand their usage and reuse them outside of the application domain they were intended for. The Discrete Event System Specification (DEVS) is a modular and hierarchical simulation formalism that clearly separates the model, simulator and experiments. It can be used break down the disciplinary silos within which single-use simulators are built and allow users to study real-world systems from a broad range of application domains. In this research, we present an architecture that facilitates the operationalization of DEVS based, geospatial simulation environments in multidisciplinary projects. The architecture relies on a clear definition of roles and responsibilities to leverage the different skillsets in an organization. It considers a series of business processes for modelers, subject matter experts, web developers and end users. It relies on a web-based architecture to provide simulation as a service capability and support users across the entire simulation lifecycle. It seeks to democratize DEVS simulation by making use of the strengths and skills available in larger organizations and by providing the necessary tools for collaboration. Importantly, it preserves key features of DEVS (genericity, modularity, flexibility, etc.) and encourages users to follow best practices in model documentation to foster model reusability and improve model discoverability. It relies on modeling and simulation as a service to overcome technological barriers of entry for DEVS simulation and provide a set of reusable tools to design simulation-based, web applications for end users.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.754
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.005

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 head0.075
GPT teacher head0.429
Teacher spread0.354 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicSimulation Techniques and ApplicationsFrench-language works237,207