Development of methods for statistical modeling of air traffic demonstrated through a Winnipeg-area case study
Why this work is in the frame
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Bibliographic record
Abstract
The integration of remotely piloted aircraft systems (RPAS) into shared airspace requires a thorough risk analysis. Specific operations risk assessment (SORA) is a widely adopted approach by international civil aviation authorities to guide RPAS operators in evaluating risks associated with their mission. A critical step in the SORA process is analyzing the airspace where the operation will take place, which requires knowledge of the intruder aircraft's flight characteristics as well as the airspace model. This paper proposes a methodology for developing a statistical airspace model using historical aircraft track data collected in the Winnipeg Manitoba Flight Information Region. The developed methods include data cleaning routines, Kalman filters for track smoothing, and Bayesian networks for synthetic track generation, following an approach similar to that employed by the Massachusetts Institute of Technology Lincoln Lab. Additionally, the developed methodology allows for the analysis of specific models by altitude or aircraft type. The methods presented were subsequently adjusted for a comprehensive analysis spanning across Canada's diverse airspace. The initial statistical model, derived from Canada-wide data, is currently accessible to the public via the National Research Council's GitHub repository [ https://github.com/nrc-cnrc/Canadian-Airspace-Models ].
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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 | 0.000 |
| 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