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Record W1592966439

Characteristics of wind turbine noise

2009· article· en· W1592966439 on OpenAlexaffvenue
Ramani Ramakrishnan

Bibliographic record

VenueCanadian acoustics · 2009
Typearticle
Languageen
FieldEngineering
TopicVehicle Noise and Vibration Control
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsTurbineNoise (video)MicrophoneWind powerAcousticsEnvironmental scienceRange (aeronautics)AmplitudeMarine engineeringWind speedMeteorologyEngineeringElectrical engineeringComputer sciencePhysicsAerospace engineeringSound pressureOptics
DOInot available

Abstract

fetched live from OpenAlex

A series of measurements were performed to address the issue associated with the characteristics of wind turbine noise. Two different types of wind turbines were monitored under different scenarios to explore the characteristics. A low frequency microphone in the operating range of 1 Hz to 10000 Hz was used for the measurement program. The main objective of the investigation was to evaluate the presence of low-frequency components below 20 Hz along with the amount of amplitude modulation of the wind turbine noise. Two areas, such as Area A and Area B were selected for the measurement program. The measurements were recorded for 15 seconds for Area A and 25 seconds for Area B.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.006
GPT teacher head0.184
Teacher spread0.178 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2009
Admission routes2
Has abstractyes

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