Prediction of noise levels and annoyance from aircraft run-ups at Vancouver International Airport
Bibliographic record
Abstract
Annoyance complaints resulting from engine run-ups have been increasing at Vancouver International Airport for several years. To assist the Airport in managing run-up noise levels, a prediction tool based on a Green's function parabolic equation (GFPE) model has been consolidated, evaluated, and applied. It was extended to include more realistic atmospheric and ground input parameters. Measurements were made of the noise-radiation characteristics of a CRJ200 jet aircraft. The GFPE model was validated by comparing predictions with results in the literature. A sensitivity analysis showed that predicted levels are relatively insensitive to small variations in geometry and ground impedance, but relatively sensitive to variations in wind speed, atmosphere type, and aircraft heading and power setting. Predicted noise levels were compared with levels measured at noise monitoring terminals. For the four cases for which all input information was available, agreement was within 10 dBA. For events for which some information had to be estimated, predictions were within 20 dBA. The predicted annoyance corresponding to the run-up events considered ranged from 1.8% to 9.5% of people awoken, suggesting that noise complaints can be expected.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".