Modeling effects of different air traffic control operational procedures, separation rules, and service disciplines on runway landing capacity
Bibliographic record
Abstract
SUMMARY This paper deals with modeling the possible effects of different advanced procedures, existing, innovative, and new air traffic control (ATC) separation rules, and service disciplines on the ultimate landing capacity of a single runway. The first implies a combination and/or exclusive use of conventional and steeper final approach and landing procedures. The second includes the current horizontal, innovative mixed horizontal/vertical and new vertical distance‐based and time‐based separation rules. The last embrace the common First Come, First Served and innovative Priority service discipline. Such increasingly complex and challenging applications are assumed to be based on the new technologies on‐board the aircraft and at the ATC to be developed in the scope of the current United States Next Generation Air Transport System and European Single European Sky ATM Research programs. The convenient analytical models for calculating the runway landing capacity are developed and applied to the generic case of a single runway according to the “what–if” scenario approach. This enables carrying out the sensitivity analysis of the landing capacity with respect to the most influential factors – the ATC advanced operational procedures, separation rules, service disciplines, and aircraft fleet mix. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".