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Record W2885654482

Comparison of International Environmental Noise Guidelines for Wind Farms

2017· article· en· W2885654482 on OpenAlexaffvenue
Sam Du, Al D. Lightstone, Joseph Doran

Bibliographic record

VenueCanadian acoustics · 2017
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsVale (Canada)
Fundersnot available
KeywordsNoise (video)Noise controlWind powerTurbineEngineeringIndustrial noiseEnvironmental noiseNoise regulationEnvironmental scienceBusinessEnvironmental planningComputer scienceElectrical engineeringNoise reductionAcousticsMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

There are various regulations, guidelines, codes of practices and best practices for assessment and control of environmental noise in various jurisdictions. Many jurisdictions treat wind farms as they do any other industrial noise source. Some jurisdictions have noise regulations/guidelines/criteria specific to wind farms. Requirements for wind turbine noise vary in strictness from country to country. The strictest noise requirements are found in Sweden, Germany, Finland, New Zealand, the United Kingdom and parts of Australia. Several countries internationally have penalties for tonal, impulsive, and low frequency noise. New Zealand, Finland and parts of Australia (Tasmania and Victoria) are found to be the only regions to include a penalty for amplitude modulation of wind turbine noise. Some jurisdictions indicate a particular acoustical modelling method (e.g. international or national standard or other technical procedure) should be used for wind farm noise assessment. Many do not specify or require a particular modelling method

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)0.011
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.161
GPT teacher head0.488
Teacher spread0.328 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2017
Admission routes2
Has abstractyes

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