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Record W1837051221

Wind turbine noise and meteorological influences

2007· article· en· W1837051221 on OpenAlexaffvenueabout
Ramani Ramakrishnan, Nicholas Sylvestre-Williams

Bibliographic record

VenueCanadian acoustics · 2007
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsWind speedWind powerMeteorologyNoise (video)TurbineEnvironmental scienceWind directionWind gradientWind profile power lawMasking (illustration)Marine engineeringEngineeringComputer scienceGeographyAerospace engineeringElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

The Ontario Ministry of the Environment (MOE) has implemented a simple procedure to assess the noise impact of a wind farm, consisting of a group of wind turbines. The process identifies the locations of the wind turbines within the wind farm as well as all the sensitive receptors within an influence zone of 1 km. It evaluates the noise levels at all identified receptor locations by using a standardized propagation model such as ISO-9613. The procedure also allows the potential masking effect of the prevailing wind noise. Meteorological data, wind speed and direction are obtained as one-hour averages from the weather station near the wind farm. The wind speeds at 10 m high are converted to a hub-height of 80 m and then reconverted back to the 10 m high wind speeds. The results show that local conditions do not follow any set patterns and there can be substantial variations in evaluated noise levels.

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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.395

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.350
Teacher spread0.319 · 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

Citations0
Published2007
Admission routes3
Has abstractyes

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