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Record W2922263937

Annoyance from wind turbine noise? Review of wind turbine noise studies of the last two decades

2018· article· en· W2922263937 on OpenAlexaboutno aff
S von Hünerbein

Bibliographic record

VenueUniversity of Salford Institutional Repository (University of Salford) · 2018
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsAnnoyanceWind powerTurbineNoise (video)Work (physics)Environmental scienceNoise pollutionRoadway noiseMeteorologyAeronauticsMarine engineeringEngineeringAcousticsComputer scienceNoise reductionGeographyAerospace engineeringElectrical engineeringLoudness
DOInot available

Abstract

fetched live from OpenAlex

In agreement with other environmental noise literature, most work on the annoyance from wind turbines has focussed on noise. Notable work has been carried out in Sweden, the Netherlands, Japan, China, Canada and the US. Their results seem to show that the noise from wind turbines starts to annoy at sound levels that are much lower than that of other sources such as road or rail traffic. At the same time other factors are identified that also correlate highly with annoyance ratings. The presentation will critically review the evidence and raise the question whether it is time to shift the focus from noise annoyance to a much broader view on the factors affecting the acceptance of wind energy installations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.303
Teacher spread0.271 · 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; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
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

Citations0
Published2018
Admission routes1
Has abstractyes

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