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Record W2903982368 · doi:10.4236/oalib.1105046

Health Canada’s Wind Turbine Noise and Health Study—A Review Exploring Research Challenges, Methods, Limitations and Uncertainties of Some of the Findings

2018· article· en· W2903982368 on OpenAlexafffundabout
Carmen Krogh, Anne Dumbrille, Robert Y. McMurtry, Richard James, Robert W. Rand, Michael A. Nissenbaum, Jeffery J. Aramini, Stephen E. Ambrose

Bibliographic record

VenueOALib · 2018
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsMcGill UniversityPrince County HospitalWestern University
FundersHealth Canada
KeywordsNoise (video)TurbineWind powerComputer scienceEngineeringAerospace engineeringArtificial intelligenceElectrical engineering

Abstract

fetched live from OpenAlex

Background: Risk of harm associated with wind turbines is debated globally.Some people living or working in proximity to wind turbines report adverse health effects such as sleep disturbance, noise annoyance, and diminished quality of life.Due to public concern, Health Canada announced its wind turbine noise and health study which included subjective and objective measurements.Findings were published between 2014 and 2016.In 2018, Health Canada published clarifications regarding the design and interpretation of study conclusions.Methods: Methods and subjective/objective findings were

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)0.088
metaresearch head score (Gemma)0.158
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score0.660

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0880.158
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0110.018
Science and technology studies0.0030.003
Scholarly communication0.0070.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.569
GPT teacher head0.545
Teacher spread0.023 · 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.

Study designNot applicable
DomainMethods
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

Citations4
Published2018
Admission routes3
Has abstractyes

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