Estimating community tolerance for wind turbine noise annoyance
Bibliographic record
Abstract
A-weighted WTN contributed less than 10% to the strength of the multiple regression model developed for wind turbine noise (WTN) annoyance in Health Canada’s Community Noise and Health Study (CNHS). Improvements required consideration of non-LAeq variables unknown or even inapplicable beyond the CNHS. To facilitate cross-study comparisons, an analysis of WTN annoyance was conducted based on the community tolerance level (CTL) model. The rate of increase in WTN annoyance was effectively estimated using a loudness function, as shown in Eq. (1). %HA = 100 exp (-(1/[10((DNL-CTL + 5.306)/10)]0.3)). (1) By convention, CTL is the DNL from Eq. (1) where 50% of the community would be highly annoyed. The CTL for WTN annoyance from field studies published to date ranges from 57.1 to 64.6 DNL (mean = 62, σ = 3). CTL values developed by others for transportation noise sources suggest that, on average, communities are between 11 dB and 26 dB less tolerant of WTN than of other sources, depending on the source. Confidence in these results should increase as future studies in this area produce additional estimates for the relationship between WTN level and the prevalence of high annoyance. The CTL analytical methods, assumptions, strengths, and limitations are presented.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".