Towards a Synthetic Environment for Maritime-Air Tactical Experiments
Bibliographic record
Abstract
The Maritime Air Littoral Operations (MALO) Technology Demonstration Program was established to research and develop a Synthetic Environment to support the development and evaluation of maritime air operational tactics, doctrine and new concept, in the prospect of the integration of new equipment within the Canadian Forces Navy - Air component. Using two technologies, MALO is providing a Modeling and Simulation based experimental environment in which tactics, doctrine and new concepts for the new Maritime Helicopter and the modernized Aurora aircraft crews can be trialed, measured and validated. MALO is developing two technologies in a four phases incremental build process. The 1st technology is a standalone physics-based simulation system, designed to support rapid experimentations, while still providing and open framework for models integration and support for external applications plug-ins. The 2nd technology is a distributed, HLA-based, high fidelity simulation to support virtual simulation experiments, and has a Computer Generated Forces capability to eventually allow for hybrid virtual-constructive simulations. This system will also provide functionalities for scenario building, entities customization, simulation controls, battlefield situational awareness, and finally data collection and analysis. Both technologies will be combined in one single experimentation process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".