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Record W2008305900 · doi:10.1121/1.4779693

Effects of circumferential air gaps on the measurement of the absorption coefficient of poroelastic materials

2002· article· en· W2008305900 on OpenAlexaff
Dominic Pilon, Raymond Panneton

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2002
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAttenuation coefficientTube (container)Materials scienceAbsorption (acoustics)PoromechanicsMeasure (data warehouse)Range (aeronautics)MechanicsAcousticsOpticsComposite materialPhysicsPorosityPorous mediumComputer science

Abstract

fetched live from OpenAlex

The influence of mounting conditions on the measurement of the absorption coefficient is investigated. More specifically, the effects of circumferential air gaps on poroelastic materials inside the standing wave tube are studied. The objective is to identify the materials, in terms of a ratio based on their physical properties, for which it is possible to measure the theoretical absorption coefficient using the tube. The difference, or error, between the measured and theoretical absorption coefficient is evaluated for a wide range of materials with various sample sizes. These errors are then sorted in terms of the chosen ratio. It is shown that for certain values of the ratio, the theoretical absorption coefficient can be efficiently measured using the standing wave tube. Through the use of this acousto-visco-inertial criteria, an experimenter can determine which absorption is going to be measured: the theoretical absorption coefficient or one that will be influenced by either the circumferential air gaps or the size of the sample.

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)0.011
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.188
Teacher spread0.178 · 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
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
Published2002
Admission routes1
Has abstractyes

Explore more

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