Army Aviation Situational Awareness Through Intelligent Agent-Based Discovery, Propagation, and Fusion of Information
Bibliographic record
Abstract
The Army Aviation community is promoting the development of technologies and systems that support effective on-the-move command of airborne and ground-based maneuver forces through shared situation awareness and decision aiding technologies. The operational concepts for these technologies and systems are characterized by the extensive use of mobile sensing systems, unmanned platforms, and decision aiding systems in the forward elements of the combat force, with the goal of providing mobile commanders with the improved situational awareness that results from a shared common operational picture of the battlefield. In support of these objectives, Lockheed Martin Advanced Technology Laboratories (ATL), under contract to the Army, is combining three ATL-developed technologies, Multi-Sensor Data Fusion, Intelligent Information Agents, and the Grapevine information sharing architecture, in an innovative way to provide the Shared Situation Awareness capability for ongoing Army programs in this area. In this paper, we describe the three technologies, their application in current and future work, and present results of simulation testing showing the benefits of Grapevine-enabled Distributed Data Fusion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".