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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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".