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Record W2167210597

Characterization of Sound Emitted by Wind Machines Used For Frost Control

2007· article· en· W2167210597 on OpenAlexaffvenue
Vince Gambino, Tony Gambino, Hugh W. Fraser

Bibliographic record

VenueCanadian acoustics · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsMinistry of Agriculture, Food and Rural Affairs
Fundersnot available
KeywordsAerodynamicsSound pressureAcousticsInfrasoundAnnoyanceNoise controlFrost (temperature)Noise (video)Sound energyWind speedEngineeringWind powerEnvironmental scienceSound (geography)MeteorologyAerospace engineeringNoise reductionComputer scienceElectrical engineeringPhysics
DOInot available

Abstract

fetched live from OpenAlex

Characterization of sound emitted by wind machines used for frost control are studied. Noise from wind machines is due to both aerodynamic and mechanical effects, but aerodynamic sounds are considered to be the most significant. The orientation of the fan changes as the sound pressure level at a fixed point in the far field changes. The sound levels vary in a sinusoidal fashion, the period being of the order of a few minutes, level changes of up to 11 dBA have been measured. Low frequency and infrasonic energy from wind machines is capable of exciting components such as floors, walls roofs and windows that comprise a building structure, thus causing increased annoyance potential.

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)0.000
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 score0.866
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.006
GPT teacher head0.203
Teacher spread0.197 · 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

Citations3
Published2007
Admission routes2
Has abstractyes

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