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Record W1605742271 · doi:10.1109/ultsym.2004.1417788

Ultrasonic wave dispersion and attenuation in a periodically two-layered medium

2005· article· en· W1605742271 on OpenAlexaff
Lu Yu, Lawrence H. Le, Mauricio D. Sacchi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAttenuationReverberationAcousticsPhase velocityScatteringPhysicsDispersion (optics)OpticsComputational physicsCutoff frequencyUltrasonic sensorMaterials science

Abstract

fetched live from OpenAlex

In this work, we use numerical method to simulate wavefield propagation through a stratified cancellous bone model. A broadband Berlage pulse with a dominant frequency of 0.9 MHz was used to simulate an ultrasonic signal traveling through the model. Based on our preliminary work of elastic cases, the simulated signal displayed strong reverberation after the main transmitted arrival, demonstrating strong scattering within the stratification. The frequency spectrum shows "periodic" passing and stopping-bands. Within the primary passing band, the phase velocity decreases with frequency and the attenuation increases mildly with frequency. Within the stopping bands, the medium supports a constant phase velocity, with maximum attenuation but wave transmission is prohibited. The first cutoff frequency increases apparently with the decrease of the thickness of the two-layered period. It is interesting that these results could be quite accurately predicted using the approximation theory, which does not consider any multiple scattering within the stratification.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.202
Teacher spread0.195 · 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 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

Citations3
Published2005
Admission routes1
Has abstractyes

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