A preliminary analysis of long-term self-reported sleep disturbance attributed to wind turbines and modelled outdoor nightly average wind turbine sound pressure level
Bibliographic record
Abstract
The World Health Organization (WHO) Environmental Noise Guidelines provide source-based annual average nighttime sound pressure level (Lnight) recommendations. For non-aircraft sources the recommendation for self-reported high sleep disturbance (HSD) is the Lnight associated with an absolute prevalence of 3%HSD. The Guideline Development Group (GDG) reported that no Lnight recommendation could be provided for wind turbines because the evidence available was inadequate. In the current study, outdoor wind turbine Lnight at each dwelling was calculated following international standards. Questionnaires were completed between May and September 2013 by individuals aged 18-79y (606 males, 632 females), randomly selected from households 0.25 to 11.22 kilometers from operational wind turbines. Calculated Lnight ranged from <25 dB(A) to 46 dB(A). When the source of sleep disturbance was unspecified, the mean prevalence of HSD was 13.3% overall and unrelated to Lnight (p = 0.53). As Lnight increased, the prevalence of identifying wind turbines as one of the causes of sleep disturbance increased from 0% below 25 dB(A) to 3.8% (95% CI 1.9% to 7.4%) between 40-46 dB(A) (p = 0.01) . The WHO's 3%HSD threshold was estimated to occur for wind turbine Lnight between 38-39 dB(A).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".