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Record W2517945028

Exterior Noise Ingress in Greater Vancouver Residential Buildings: Prediction versus Field Measurements

2016· article· en· W2517945028 on OpenAlexvenueaboutno aff
Gary Mak, Mark Bliss

Bibliographic record

VenueCanadian acoustics · 2016
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsFacadeNoise (video)Noise controlNoise measurementEngineeringCivil engineeringComputer scienceEnvironmental scienceTransport engineeringNoise reduction
DOInot available

Abstract

fetched live from OpenAlex

Most municipalities in the lower mainland prescribe acoustic requirements for rezoning applications to study outdoor noise ingress and necessary facade improvements to meet interior noise limits such as those mentioned in the CMHC Road and Rail Noise: Effects on Housing criteria. However, there is no standard method of performing this assessment. As there are often no requirements for post-construction testing, the actual interior noise levels are not measured and compared with the initial predictions. After a number of assumptions and theoretical calculations, how accurate are these interior noise predictions? For a number of residential development projects, we conducted post-construction measurements to measure the actual noise level inside bedrooms. The measurement results were compared with the original predictions to assess the accuracy and identify tendencies to over- or under-predict. In addition, we looked at factors which could influence the accuracy such as facade noise exposure estimate, transmission loss data, and noise ingress calculation. This paper discusses our findings and recommended best practices for interior noise level predictions for Greater Vancouver residential construction.

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 score0.479
Threshold uncertainty score0.937

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 head0.015
GPT teacher head0.197
Teacher spread0.182 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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