Using the Coalition Battle Management Language Standard for Interacting with the Restful Interoperability Simulation Environment
Bibliographic record
Abstract
Computer simulation is used for planning and training for cases where the real-life scenario is expensive, rare or dangerous.Interoperability techniques allow diverse models built using different technologies on different hardware platforms to interact to create a larger, more complex synthetic environment.The Coalition Battle Management Language (C-BML) standard provides a set of definitions that can be used to communicate a commander's intent.The Military Scenario Definition Language (MSDL) uses the same building blocks to construct an initial definition of a scenario that is to be executed.The Discrete Event Simulation (DEVS) methodology provides a technique for modeling systems that react to external input in the form of events.The RESTful Interoperability Simulation Environment (RISE) provides a webenabled platform that hosts the execution of DEVS models.This thesis proposes an architecture which adds the capability of using a structured scenario definition language file based on MSDL to initialize a DEVS model, and to provide a structured message based on C-BML as the initial input to a DEVS model.This architecture is validated through the execution of a scenario where civilian emergency services respond to an emergency.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.011 |
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".