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Record W2888986874 · doi:10.1121/1.5051331

Wind turbine low frequency and infrasound propagation and sound pressure level calculations at dwellings

2018· article· en· W2888986874 on OpenAlexafffundabout
Stephen E. Keith, Gilles A. Daigle, Michael R. Stinson

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2018
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsHealth Canada
FundersHealth Canada
KeywordsInfrasoundSound pressureEnvironmental scienceMeteorologyTurbineNoise (video)Wind speedAcousticsAtmospheric sciencesPhysicsComputer science

Abstract

fetched live from OpenAlex

This study was developed to estimate wind turbine low frequency and infrasound levels at 1238 dwellings in Health Canada's Community Noise and Health Study. In field measurements, spectral peaks were identifiable for distances up to 10 km away from wind turbines at frequencies from 0.5 to 70 Hz. These measurements, combined with onsite meteorology, were in agreement with calculations using Parabolic Equation (PE) and Fast Field Program (FFP). Since onsite meteorology was not available for the Health Canada study, PE and FFP calculations used Harmonoise weather classes and field measurements of wind turbine infrasound to estimate yearly averaged sound pressure levels. For comparison, infrasound propagation was also estimated using ISO 9613-2 (1996) calculations for 63 Hz. In the Health Canada study, to a distance of 4.5 km, long term average FFP calculations were highly correlated with the ISO based calculations. This suggests that ISO 9613-2 (1996) could be an effective screening method. Both measurements and FFP calculations showed that beyond 1 km, ISO based calculations could underestimate sound pressure levels. FFP calculations would be recommended for large distances, when there are large numbers of wind turbines, or when investigating specific meteorological classes.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.692

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 studies0.0010.001
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 head0.028
GPT teacher head0.329
Teacher spread0.300 · 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 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

Citations25
Published2018
Admission routes3
Has abstractyes

Explore more

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