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Record W2093991172 · doi:10.1121/1.1979447

Impedance and Brewster angle measurement for thick porous layers

2005· article· en· W2093991172 on OpenAlexaboutno aff
Craig J. Hickey, Del Leary, Jean F. Allard, Michel Henry

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2005
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsnot available
Fundersnot available
KeywordsBrewster's angleMaterials scienceElectrical impedanceOpticsReflection coefficientPorosityWork (physics)Measure (data warehouse)Angle of incidence (optics)Porous mediumAcousticsReflection (computer programming)ConductivityBrewsterComposite materialPhysicsComputer science

Abstract

fetched live from OpenAlex

For thin nonlocally reacting porous layers, a method derived from the work of [Chien and Soroka, J. Sound Vib. 43, 9–20 (1975)] has been used to localize the pole of the reflection coefficient located at a complex angle close to π∕2 and to measure the surface impedance at this angle. Measurements are performed with a small source/receiver separation. The method is used in the present work to measure the surface impedance of acoustically thick porous layers of high flow resistivity. Simulations show that the measured impedance, which is related to a complex angle close to π∕2 like for thin porous layers, is close to the impedance at grazing incidence. It is also shown that for semi-infinite layers the method provides a measure of the Brewster angle of the medium. Measurements of the cosine of the complex angle and of the related impedance on two granular media of high flow resistivity, Ottawa sand and glass beads, are in a reasonable agreement with predictions for frequencies above 1kHz for a source/receiver separation of 30cm.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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

Citations8
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicAcoustic Wave Phenomena ResearchFrench-language works237,207