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Record W2771579578 · doi:10.3397/1.3703101

Controlling air-borne and structure-borne sound in buildings

2011· article· en· W2771579578 on OpenAlexaff
J. D. Quirt

Bibliographic record

VenueNoise News International · 2011
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSound (geography)Environmental scienceAcousticsArchitectural engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

In recent years, the science and engineering for controlling sound transmission in buildings have shifted from a focus on individual assemblies such as walls or floors, to a focus on performance of the complete system. Standardized frameworks for calculating the overall transmission including structure-borne flanking, combined with standardized measurements to characterize sub-assemblies, have advanced these issues from research concepts to engineering practice in many countries. From studies of relatively homogeneous and isotropic constructions of concrete and masonry in the 1990's, the technology is now expanding to include the more complicated behavior of lightweight framed constructions. These advances in measurement-based calculations offer the potential for better design based on comprehensive prediction of sound transmission between units in multifamily buildings. To realize that potential, we still must overcome several challenges. First, the acoustical prediction tools must be suitable for designers who integrate the many aspects of building performance. Second, the acoustical metrics must properly reflect how occupants respond to transmitted sound from both typical airborne sources and impact sources such as footsteps. These concerns pose major challenges for the next decade - both for research and for implementation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score0.999

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)0.0020.000

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 head0.040
GPT teacher head0.358
Teacher spread0.318 · 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.

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

Citations2
Published2011
Admission routes1
Has abstractyes

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