Quantitative Risk Evaluation of Obstacle Limitation Surfaces for Final Approaches at Airports
Bibliographic record
Abstract
Obstacle limitation surfaces (OLS) are the main safeguard against objects that can pose a hazard to aircraft operations at and around airports. The standard dimensions of the most of those surfaces were estimated using the pilot’s experience at the time when they were included in the standard documents. As a result, some of these standards may have been overestimated, while others may not provide an adequate level of safety. With airports moving to the Safety Management System (SMS) approach to design and operations safety, proper evaluation of the level of safety provided by OLS at specific sites becomes important to airport operators. Therefore, this study attempts to collect actual flight path data using information provided by air traffic control radars and to construct a methodology to assess the probability of aircraft deviating from their approach path. This will be helpful to estimate safe and efficient standard dimensions of the OLS and assess the risk level of objects to the aircraft operations around airports. The methodology is presented using the aircraft trajectories of approaches at Ottawa International Airport (CYOW). Estimated dimensions of Code 3 approach surfaces also are presented.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".