Trajectory Accuracy Sensitivity to Modeling Factors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents the analysis of sensitivity of aircraft trajectory prediction to relevant modeling factors such as wind, temperature, thrust settings, speed and aircraft mass. The topic is important because current and planned automation systems used for air traffic management, airline operations and Flight Management Systems rely on the accuracy of four dimensional trajectories (4DT) to plan and manage operations. Trajectory accuracy becomes even more critical in the Trajectory Based Operations (TBO) concepts envisioned for the Next Generation Air Transportation system (NextGen). Understanding the predictability envelope under various realistic conditions provides insight into the potential benefits and limitations of the efficiency gains that are expected from TBO. The aim of this study is to perform a systematic analysis of the accuracy of trajectory predictions across a wide range of aircraft types and operating environments. This is done by Monte Carlo simulations of the trajectory generation process that take into account the error distributions of the input variables to the trajectory generation algorithms. The prior distributions are calibrated using empirical data. The Monte Carlo method is used to generate the posterior distributions of trajectory accuracy performance metrics.
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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 | 0.001 |
| 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