Comparison of Air Craft Noise Exposure in between NEF Contour Prediction and 24-Hour Measurement Results
Bibliographic record
Abstract
BKL Consultants presents her latest findings on the difference in noise exposures in between data collected from long-term noise measurements and predictions from NEF contours. This study was performed in the Greater Vancouver area where noise levels caused by aircraft activities related to Vancouver International Airport (YVR) are major concerns. YVR previously selected a list of locations for their long-term noise monitoring program. BKL’s analysis focuses on these locations and areas nearby. YVR publishes annual equivalent noise levels at these selected locations. BKL used these annual monitoring results to compare with the predicted noise exposures at the same locations from NEF contours. 24-hour noise measurement results were also used to compare with YVR’s annual equivalent noise levels. A statistical analysis on the 24-hour noise measurement results was performed in order to investigate on daily variations. From BKL’s study, noise levels predicted by NEF contour are usually more conservative. BKL has discussed about her practical ideas on residential and commercial developments in regions near YVR as well.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".