A Web Based Architecture to Operationalize Geospatial Simulation Environments
Bibliographic record
Abstract
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.)
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".