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Record W4406422787 · doi:10.1121/10.0034710

An analysis of self-reported sleep disturbance from nighttime wind turbine noise suggests minimal effects but highlights the need for standardization in research design

2025· article· en· W4406422787 on OpenAlexaffabout
David S. Michaud, Mireille Guay, Stephen E. Keith, Allison Denning, James P. McNamee

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2025
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsHealth Canada
Fundersnot available
KeywordsStandardizationDisturbance (geology)Noise (video)TurbineSleep (system call)Sleep disorderComputer scienceEngineeringPsychologyArtificial intelligenceAerospace engineeringGeologyPsychiatryInsomnia

Abstract

fetched live from OpenAlex

The World Health Organization Environmental Noise Guidelines provide source-based nighttime sound level (Lnight) recommendations. For non-aircraft sources, the recommended Lnight is where the absolute prevalence of high sleep disturbance (HSD) equals 3%. The Guideline Development Group did not provide an Lnight for wind turbines due to inadequate data. In the current study, calculated outdoor wind turbine Lnight levels ranged from <20.5 to 41.5 dB(A). Between May and September 2013, questionnaires were completed by 606 males and 632 females, 18-79 years of age, randomly selected from households 0.25 to 11.22 km from operational wind turbines. 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, identifying wind turbines as one of the causes of HSD increased from 0% below 20.5 dB(A) to 3.8% between 35.5-41.5 dB(A) (p = 0.01). The 3%HSD benchmark was observed where Lnight was 33.5 dB(A) [95% confidence interval (CI) 31.1-36.1 dB(A)]. Results affirm findings from Health Canada's Community Noise and Health Study of minimal impacts of wind turbines on sleep [Michaud et al. (2016a). "Effects of wind turbine noise on self-reported and objective measures of sleep," Sleep 39(1), 97-109], yet noted uncertainties and limitations are discussed, including the suggestion that the HSD benchmark for wind turbines may be too low.

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 imitation

Not 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.

metaresearch head score (Codex)0.053
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.282

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.083
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.034
GPT teacher head0.393
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainMethods
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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