Understanding the human impact caused by the sound of wind turbines
Bibliographic record
Abstract
A mystery surrounds the human impact reported by those with wind turbines in their environment. “PubMed” identifies 23 papers for “wind turbine human health.” A dose-response relationship between exposure to sound, annoyance, and sleep disturbance is generally accepted. Self-reporting identified other impacts that commenced or increased with wind turbines operation. Recent articles repeat that no peer-reviewed papers show other links than annoyance and possibly sleep disturbance, and suggest that wind turbine visibility, negative attitudes, fear, or lobby groups cause adverse reporting, but provide no evidence dispelling impacts. Many argue acceptability of annoyance is a social measure set by government. Meantime, the Government of Canada “Wind Turbine Noise and Health Study” presented findings of an association between increasing noise from wind turbines and annoyance, but no found no evidence linking exposure to wind turbine noise to any of the self-reported illnesses, and no association between wind turbine noise and measures of stress, sleep quality, or significant changes in quality of life. Neglected in all of this, those who have been adversely impacted believe no one is listening. This paper takes up their position, to examine other acoustic factors to generate a hypothesis for cause of the human impact.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".