Creating Spatially-Shaped Defense Models Using DEVS and Cell-DEVS
Bibliographic record
Abstract
In recent years, new techniques for military modeling and simulation provided the practitioner with advanced mechanisms to describe complex applications. Some of the recent efforts in the field tried to address important issues in open research areas, ranging from agent-based modeling, multiresolution/hierarchical models, hybrid models, and composability. We show how to address some of these issues through the application of a formal modeling and simulation technique and its application to the domain of defense applications. Our efforts consider the construction of multimodels, including components that can be defined as spatially-shaped models, using the Cell-DEVS and DEVS formalisms. DEVS is a mathematically sound framework in which a system is modeled by dividing it into a number of components (each of them having a discrete state and interacting with the environment via input/output ports). Cell-DEVS is an extension to DEVS that formulates the execution of cellular models with explicit timing delays. We show how these concepts can be applied to different defense-related spatial models, including a radar transmitter/receiver, a target-seeking device, and land battlefield models.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".