Use of aircraft derived data for more efficient ATM operations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How to make the air-transport system more efficient? The answer is complicated and yet simple: provide the most accurate data concerning the flight and share that information between the actors in the air transport system! Indeed this is foreseen in the ICAO operational concept adopted at the eleventh Air Navigation Conference in Montreal in September/October 2003. Provision of aircraft derived data (ADD) is not a new idea, but recent technological progress makes it a far more realistic proposition, especially since most modern aircraft have much more accurate information than the ground system concerning their actual status and its projection to the future. This paper discusses the ways in which ADD can be used to benefit air traffic management. Potential ADD benefits result from reductions in controller workload through the provision of controller access parameters and also the enabling of more accurate trajectory predictions which should improve the efficiency of air traffic planning and monitoring tools. ADD can also facilitate the interaction of ATC with airline operation centres and airport operations. The work presented here is part of an ongoing European Union-funded NEAN Update Programme (NUP) (Gustavsson, 2001) activity to determine the technical feasibility of downlinking ADD to ground ATC systems using ADS-B and the operational benefits that this would bring. An operational service description has been developed (ADD Tiger Team, 2004) specifying how ADD could be used in ground ATC en-route and terminal area systems. Validation studies are ongoing focusing on the potential improvements to trajectory prediction that can be obtained through the use of ADD and the resulting efficiency benefits on controller decision support tools.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it