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Record W1955395435

Three-microphone two-cavity method for measuring sound transmission loss in a modified impedance tube

2011· article· en· W1955395435 on OpenAlexaff
Yacoubou Salissou, Raymond Panneton, Olivier Doutres

Bibliographic record

VenueEspace ÉTS (ETS) · 2011
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAcousticsMicrophoneLoudspeakerSound transmission classTransmission lossElectrical impedancePiston (optics)Acoustic impedanceTube (container)Discontinuity (linguistics)EngineeringMaterials scienceOpticsPhysicsElectrical engineeringMathematicsMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

The normal incidence sound transmission loss (nSTL) is an important indicator to assess the sound insulation property of acoustic materials. The apparatus consists of a finite length rigid walled impedance tube with circularly shaped and uniform inner cross-section. The tube features a loudspeaker at one end and a movable piston at the other end. The loudspeaker is used to generate a plane wave field in the impedance tube. There are two microphones flush mounted upstream the test sample and one microphone flush mounted on the rigid end. Next, a 20-mm thick step discontinuity seeing as a non-symmetrical sample is tested and compared to the standard 4M2L method and 3M-TMTC method. One can note that similar results are obtained between the 3M2L and 4M2L methods, however 4M2L is noisier compared to 3M2L.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.052
GPT teacher head0.295
Teacher spread0.243 · 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 designBench or experimental
Domainnot available
GenreMethods

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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