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Record W4400286352 · doi:10.1121/10.0026760

Introducing the sound transmission loss suite at the British Columbia Institute of Technology

2024· article· en· W4400286352 on OpenAlexaffabout
Won Suk Ohm, Omid Tamanna

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsSuiteSound (geography)Transmission (telecommunications)Sound transmission classTelecommunicationsAcousticsEngineeringComputer scienceHistoryArchaeologyPhysics

Abstract

fetched live from OpenAlex

A sound transmission loss suite is a facility for measuring the airborne sound insulation by building elements such as wall assemblies and partitions. It consists of two neighboring reverberation chambers (called the source and receiving rooms), where the only significant sound transmission path is presented by the test specimen, enclosed in the common wall separating the two chambers. In this talk, an overview of the sound transmission loss suite that was newly built and commissioned at the British Columbia Institute of Technology in late 2023 is given. The suite is comprised of two reverberation chambers with interior volumes of 200 m3 (source room) and 125 m3 (receiving room) and can test for frequencies from 63 to 20000 Hz, making it one of a kind in Western Canada. The talk touches upon the acoustical characteristics of the suite and the relevant test method for measuring transmission loss according to the ASTM E90 standard. [Work supported by Canada Foundation for Innovation—Project No. 36346.]

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.455
Threshold uncertainty score0.905

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0140.007

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 head0.008
GPT teacher head0.238
Teacher spread0.230 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes2
Has abstractyes

Explore more

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