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Record W1999058868 · doi:10.1121/1.4779348

Diffusing acoustic wave spectroscopy

2002· article· en· W1999058868 on OpenAlexaff
J. H. Page, M. L. Cowan, David A. Weitz

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicSeismic Waves and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCodaPhysicsScatteringSpectroscopyComputational physicsMean free pathAcoustic waveDiffusionRange (aeronautics)InterferometryMeasure (data warehouse)OpticsAcousticsMaterials scienceQuantum mechanics

Abstract

fetched live from OpenAlex

Diffusing Acoustic Wave Spectroscopy (DAWS) is an ultrasonic technique that has been developed to measure the dynamics of heterogeneous media from the temporal fluctuations of multiply scattered waves. This technique is similar to more recent developments in field fluctuation spectroscopy, called coda wave interferometry, that use variations in the seismic coda to infer changes in the medium with time. After reviewing the basic principles on which Diffusing Acoustic Wave Spectroscopy is based [M. L. Cowan, J. H. Page, and D. A. Weitz, Phys. Rev. Lett. 85, 453 (2000); Phys. Rev. E 65, 066605 (2002)], its potential as a sensitive method for probing the dynamics of strongly scattering materials will be illustrated with recent experiments on fluidized suspensions of particles. In this type of system, DAWS measures the local relative motion of the scatterers (or strain rate) on a length scale determined by the transport mean free path of the multiply scattered waves. When combined with the complementary technique of Dynamic Sound Scattering using singly scattered waves, DAWS can also determine the instantaneous velocity correlation length of the moving scatterers, thus giving a quite complete picture of the system dynamics over a wide range of length and time scales.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.463
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.212
Teacher spread0.196 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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

Citations1
Published2002
Admission routes1
Has abstractyes

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