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Record W2332066090 · doi:10.1121/1.4942405

Wind turbine sound power measurements

2016· article· en· W2332066090 on OpenAlexafffundabout
Stephen E. Keith, Katya Feder, Sonia A. Voicescu, Victor Soukhovtsev, Allison Denning, Jason Tsang, Norm Broner, Werner Richarz, Frits van den Berg

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2016
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsTransport CanadaHealth Canada
FundersSamsungMinistry of Natural Resources
KeywordsTurbineAcousticsSound (geography)Noise (video)Sound powerWind powerPower (physics)Environmental scienceStandard deviationMarine engineeringMathematicsStatisticsComputer scienceEngineeringElectrical engineeringPhysicsAerospace engineering

Abstract

fetched live from OpenAlex

This paper provides experimental validation of the sound power level data obtained from manufacturers for the ten wind turbine models examined in Health Canada's Community Noise and Health Study (CNHS). Within measurement uncertainty, the wind turbine sound power levels measured using IEC 61400-11 [(2002). (International Electrotechnical Commission, Geneva)] were consistent with the sound power level data provided by manufacturers. Based on measurements, the sound power level data were also extended to 16 Hz for calculation of C-weighted levels. The C-weighted levels were 11.5 dB higher than the A-weighted levels (standard deviation 1.7 dB). The simple relationship between A- and C- weighted levels suggests that there is unlikely to be any statistically significant difference between analysis based on either C- or A-weighted data.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.643
Threshold uncertainty score0.632

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.044
GPT teacher head0.354
Teacher spread0.310 · 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 designNot applicable
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

Citations30
Published2016
Admission routes3
Has abstractyes

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