Bibliographic record
Abstract
Almost without hesitation, most people can identify a sound that is annoying to them, whether it might be fingernails on a chalkboard, a barking dog late at night, a mosquito buzzing in their ear, or their own particular example. Classic acoustics texts identify key points related to annoyance. These special characteristics of include tonality, a non-random cyclical nature, pitch, roughness, rise time, and dominance of noise during sleeping hours when environmental noises diminish. A new source of environmental sound arises from wind turbines, a rapidly growing method of generating electricity. Studies such as the “Health Canada Wind Turbine Noise and Health Study” [1] have documented noise annoyance complaints. This paper categorizes wind turbine noise complaints based on face-to-face interviews with impacted individuals, and correlates logs of complaints to conditions at the time. Recordings made in a controlled manner of environmental sound samples, such as flowing streams, wind in coniferous trees, or wind in bare or leafed deciduous trees as well as other sounds found in the environment, such as vehicles passing by on highways, aircraft overhead, and railway travel are compared with sound recordings from wind turbines. The comparisons included analysis of LZeq, LAeq, narrow band analysis, evaluation of amplitude and frequency modulation, and fluctuation strength. Development of modifiers to normal LAeq sound limits is suggested to improve the effectiveness of regulations. A key finding shows annoyance is related more to changes and characteristics at a particular time, rather than to longterm averages of sound. Why annoying sounds matter is a complex subject. Some consider “annoying” has little impact more than, “your gum chewing is annoying,” while for others, an annoying sound can mean loss of sleep, and loss of that restorative time itself has many documented adverse effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".