Aircraft noise contours—From everything for everyone to nothing for anyone
Bibliographic record
Abstract
Aircraft noise exposure contours were originally intended as a tool to facilitate compatible land use in the vicinity of airports. The simple concept was for authorities to use historic data and reasonable predictions for near future demands to create a map that identified areas of high aircraft noise impacts. Municipalities and planners would refer to this map to determine appropriate zoning around the airport, restricting noise sensitive development in high noise exposure areas. With time, different demands and applications of the noise contours emerged. Some stakeholders demanded longer term forecasts to allow for planning farther into the future. Some co-opted contours as a PR tool to suggest reduced impacts on communities. Some began using noise contours as a tool to protect against encroachment. Others began to look to noise contours as a representation of daily acoustic conditions in areas surrounding the airport. In Canada, a lack of oversight and guidance for the selection of inputs parameters for noise models, make it such that noise contours have become a product illustrating whatever their creator intends, lacking objectivity and scientific rigour. This research demonstrates how noise contours can be designed to achieve desired results by modulating different input parameters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".