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Record W4400287867 · doi:10.1121/10.0027355

Ongoing confirmation of an objective criterion predicting annoyance linked to wind turbines

2024· article· en· W4400287867 on OpenAlexaboutno aff
William K. Palmer

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldEngineering
TopicMachine Fault Diagnosis Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsAnnoyanceWind powerComputer scienceEnvironmental scienceReliability engineeringEngineeringAutomotive engineeringElectrical engineeringComputer vision

Abstract

fetched live from OpenAlex

The 2023 CAA Acoustics Week in Canada introduced a criterion based on an objective acoustic measure to predict annoyance subjectively identified by residents living in the vicinity of wind turbines. That evidence was gathered primarily at a site near constant speed, stall regulated wind turbines. This paper presents subsequent investigations confirming that the criterion is also effective at a site with variable speed pitch regulated wind turbines. The results arise from the analysis of over 400 days of sampling at a site 787 m from the nearest wind turbine, with 18 turbines within 3 km. Verification of the study data was shown by comparison to data collected by an acoustic contractor employed by the provincial Ministry of the Environment. That data was collected through over 70 recording periods to analyze times residents at the site identified annoyance during a Ministry audit. These samples were obtained through a Freedom of Information request purchase. Analysis of the samples confirms the annoyance criterion identified at the CAA 2023 conference applies also for a different turbine type to objectively predict the annoyance subjectively identified by residents. The implications of this confirmation will be discussed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.280
Teacher spread0.272 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

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Same venueThe Journal of the Acoustical Society of AmericaSame topicMachine Fault Diagnosis TechniquesFrench-language works237,207