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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".