Prototype Unmanned System Training Simulator
Bibliographic record
Abstract
This paper presents a prototype training simulator design developed in response to the expanding student population for unmanned system operators. The projected operators for unmanned systems are now being expanded beyond the traditional focus-group of pilot and pilot trainees. The broadening of the field from which unmanned system operators previously had been selected, and the increased mission support role of operators, also broadens the ground training requirements in order to achieve certification. The backbone of the simulator for this training system is a scalable architecture concept that is softwareintensive with loosely coupled training system elements. This common backbone for scalable application also results in common logistic support, meaning lower life cycle cost. The prototype training device design takes advantage of commercially off-the-shelf (COTS) hardware and software products already proven in fielded platforms. The training system design that responds to these requirements incorporates principles from device-based aircrew training as well as high engagement strategies from simulation and gaming. This design not only enhances unmanned system operator training, but also makes significant advancement of role player in-the-loop mission training.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".