Impacts of wind turbine farm obscurations on aircraft escort probability of success
Bibliographic record
Abstract
The presence of large wind turbine farms has been shown to significantly degrade radar tracking of aircraft. The loss in localized radar coverage could pose issues in airspace management, especially within Temporary Flight Restrictions (TFR) areas. As wind turbine development expands, there is an increasing potential that the degraded radar tracking surveillance could negatively impact safety operations. Two analytical approaches are considered to compute the probability of successfully intercepting and escorting an unauthorized aircraft away from TFR controlled areas near wind turbines. New models, which were specifically designed to address wind turbine interference with ground-based radars, are utilized to simulate both the losses in radar tracking continuity from wind turbine obscuration and the resulting impact this has on airspace safety operations. Probability distributions are used to model intercept / escort processes including interceptor take-off times. A probability of success expected value is computed for candidate routes over a range of aircraft velocities. The intercept sequences are modeled under various conditions (no turbines, existing turbines, and expected future turbine development) to measure the contrast in probability of success lost as a direct result of turbines. Both Monte Carlo and convolutional “Direct Probability” approaches are considered.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".