Differences in Predicted Far-Field Sound from Wind Turbine Noise Sources having Comparable Overall A-Weighted Sound Power Levels using ISO 9613-2
Bibliographic record
Abstract
This paper is based on research conducted by the author through Aercoustics Engineering Limited (Mississauga, Ontario, Canada). The A-weighting curve is a widely adopted method by which sound levels are adjusted to account for the human perception to the sound. This weighting curve applies increasing levels of attenuation for sound at frequencies below 1 kHz. The sound power emission of a given piece of mechanical equipment is often given in terms of an overall A-weighted power level, a logarithmic sum of each 1/3 octave component of the frequency spectra. Due to the nature of this summation, two pieces of equipment may yield similar overall sound levels while having vastly different low-frequency spectral content. This study compares the difference in predicted far-field noise levels from wind turbines that have different 1/3 octave spectra but comperable overall A-weighted sound levels. The focus of this study was on the propogation of wind turbine noise, modelled per ISO 9613-2, using published sound power data from various turbine manufacturers. The impact from wind turbines of similar overall A-weighted sound power ratings was assessed at points of reception placed at varying distances from the turbines.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".