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Record W2622373660 · doi:10.1121/1.4987245

Measurement data for the prediction of the flanking transmission in lightweight building constructions

2017· article· en· W2622373660 on OpenAlexaffabout
Jeffrey Mahn, Christoph Höller

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2017
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsFlanking maneuverIsotropyHomogeneousReduction (mathematics)Transmission (telecommunications)Path (computing)Computer scienceAcousticsSound transmission classRadiationStatisticsMathematicsTelecommunicationsPhysicsOpticsStatistical physicsStructural engineeringEngineeringGeometry

Abstract

fetched live from OpenAlex

The ISO 15712 series of standards describe a method of predicting the flanking transmission in homogeneous isotropic building constructions. Since the method was first published in 1979, there has been great interest in applying the prediction method to lightweight constructions which are neither isotropic nor homogeneous. The prediction method becomes more complicated for lightweight constructions because the resonant sound reduction indices of the elements must be estimated from measurement data including the sound reduction indices and the resonant and total radiation efficiencies. However, there is the question of which sound reduction index and radiation efficiencies should be used. Should they be for the whole wall or a single panel? Results from a study conducted at the National Research Council Canada are presented. Predicted values of the flanking transmission loss for each flanking path are compared to data which was measured in the National Research Council's eight room flanking facility.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.052
GPT teacher head0.288
Teacher spread0.236 · 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.

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

Citations0
Published2017
Admission routes2
Has abstractyes

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