Simulation-C2 interoperability through data mediation: the virtual command and control interface
Bibliographic record
Abstract
As C2 systems become more sophisticated, constructive simulation is necessary to provide credible battle scenarios at a useful level of fidelity and resolution for training and experimentation. Interoperability, in the form of automated and semi-automated information exchange between the simulation and C2 domains, is essential.This paper describes an approach to simulation-C2 interoperability employed by the Canadian Department of National Defence (DND). For the past several years, DND has developed the Virtual Command and Control Interface (VCCI), influenced largely by the evolution of C2 systems built on the Multilateral Interoperability Programme (MIP) Information Exchange Data Model (IEDM). VCCI is now fielded and in use within the Canadian and UK Armies. In the VCCI framework, simulation-specific components handle translation between the simulation(s) (such as the Joint Conflict and Tactics Simulation (JCATS) and others) and a IEDM-compliant database. This VCCI database, or VDB, becomes the hub for data exchange; it not only contains information from the simulation(s) and C2 system in use, but also meta-information about the larger training/experimentation context. Data exchange can then performed with the IEDM-compliant database used by the C2 system (in Canada s case, the Operational Database). Although this exchange can be largely automated, our experience has shown the value of maintaining a mediation agent, allowing human-in-the-loop data mediation in order to achieve training or experimentation objectives. By separating the data translation function (from the simulation to the VDB) from the information management function (between the VDB and the ODB), the VCCI framework allows each function to be controlled by the appropriate parties. Also, by maintaining contextual information within the VDB, after-action review (AAR) can be enriched.The current state of the VCCI architecture is presented and described, recent experiences with VCCI use in preparing units for operational deployments are discussed, and areas for future exploitation are identified.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".