Crosswind‐based optimization of multiple runway orientations
Why this work is in the frame
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Bibliographic record
Abstract
Summary The runway orientation must satisfy the operational requirements of aircraft for landing and takeoff. Actually, the runway orientation is the result of compromises between the airport usability (wind coverage) and additional factors, such as available land, existing obstructions, topographic difficulties, flight path interference among runways and airports, noise pollution, and other environmental impacts. Therefore, the solution of a combination of acceptable runway orientations, which avoids excessive crosswinds at least 95% of the time, as well as the optimal orientation solution, is essential to conduct those compromises in the runway orientation analysis. The objective of this paper is to develop a computer model, named the optimization of multiple runway orientations model, which is capable of simultaneously providing a combination of acceptable runway orientations, changing the allowable crosswind limit flexibly, and determining the optimal orientations of multiple runway configurations. Instead of visual estimation or geometric computation, this paper presents an analytical method for wind coverage analysis. The model is mainly running in spreadsheet and Visual Basic for Applications (VBA). The numerical example and comparison show that the optimization of multiple runway orientations model is competitively accurate and convenient in comparison with previous ones. This paper presents an up‐to‐date model for the optimization of multiple runway orientations. By combining it with the geographic information system obstructions model, it can become an essential element of a future model for airport development cost minimization that combines airfield land use, earthwork volume, and cost estimation modules. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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