Synthetic Environments at the Enterprise Level: Overview of a Government of Canada (GoC), Academia and Industry Distributed Synthetic Environment Initiative
Bibliographic record
Abstract
A new partnership between the Government of Canada (GoC), Industry and Academia is working to help Canada become a world leader in Modeling and Simulation (M&S). The goal is to take SMARRT technologies - Simulation and Modeling for Acquisition, Requirements, Rehearsal and Training - and apply them to the development and improvement of Canadian Forces (CF) military systems and capabilities, as well as a vast host of civilian applications. To this end, the initial partners conceived the October 2004 UAV-NTS SE Interoperability Experiment, which links DRDC Ottawa's Uninhabited Air Vehicle Research Test Bed (UAV RTB) to a Carleton University Networked Tactical Simulator (NTS) modified with CAE Inc. synthetic environment (SE) assets including computer-generated forces. The experiment took place via a non-dedicated unclassified, though VPN-encrypted network over a period of several days. Prospective GoC, Academia and Industry partners, as well as foreign military personnel, attended these sessions. The goal was to show how M&S technologies can be used and made relatively easy at the national/enterprise level, and how lessons learned and technical best practices point to a bright future for distributed synthetic environment applications in Canada's Department of National Defence, as well as for DND's public security partners.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.003 |
| 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".