Use of aircraft derived data for more efficient ATM operations
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.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".