Risk Assessment Methodology for Runway End Safety Area (RESA) at Canadian Airports
Bibliographic record
Abstract
In response to recent domestic and international developments, Transport Canada Civil Aviation (TCCA) published Notice of Proposed Amendment (NPA) 2010-012 to mandate the implementation of Runway End Safety Areas (RESAs) at certain certificated airports. This is intended to harmonize the airport requirements for a RESA with the International Civil Aviation Organization (ICAO) standards. As proposed in revised NPA, a RESA would be required if the runway is longer than 1200 m or if an instrument runway is utilized by passenger carriers with more than 9 passenger seats. As a result of industry feedback to the NPA and to better document the risks and safety benefits associated with RESA, TCCA released a request for proposal (RFP) for an independent risk assessment study. GENIVAR in combination with Applied Research Associates (ARA) was selected to conduct the study. The main objectives of the study are the following: (1) Develop a high level qualitative risk assessment model of runway overrun and undershoot; (2) Develop a consequence model for aircraft overrunning and undershooting a runway; (3) Develop a database of certificated airports runways to include major operational characteristics as well as RESA characteristics through surveys; and (4) Apply the consequence model to the database both in current RESA condition and in compliant condition. This paper presents the methodology that is developed for the risk assessment as it is pertinent to takeoff overrun events. Similar methodology can be used for the assessment of the risk for landing overrun and landing undershoot accidents. The risk assessment methodology consists of evaluating the likelihood of a takeoff overrun event based on historic accidents that have happened in Canada and combining that with a consequence model that is also derived from historic events. The paper also presents how the methodology could be implemented to assess the risk of overrun at Canadian airports responding to a questionnaire.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".