Bibliographic record
Abstract
Current research envisions shifting the role of flight crews towards mission supervisors who make decisions at a very high level of abstraction -- decisions that guide complex systems automatically towards a defined goal. The applicable aircraft condition and contextual information towards the development of smarter Automatic Flight Control Systems (AFCSs) supporting this vision are highlighted. These include the aircraft's systems and capabilities state, the airspace structure, weather and traffic situation, the surrounding terrain and its population density, facilities, as well as human factors and operational aspects. The presented concept particularly aims at integrating Air Traffic Control (ATC) and the operational environment into the automatic decision making process. Suitable Artificial Intelligence (AI) methods and algorithms shall be studied and evaluated on a small commercially available Unmanned Air Vehicle (UAV). The Unmanned Aircraft System (UAS) will be extended to support simulated interactions with ATC and mission control. The resulting system shall be able to perform missions on the basis of abstract goal descriptions that may change during the flight and require revised online flight planning and an adapted aircraft systems configuration in a hard real-time environment constrained by bounded rationality and bounded reactivity. Such an UAS will enable higher-level command and control as well as increasingly flexible airborne missions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".