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Record W3206411144 · doi:10.1121/10.0006535

The effective medium for a cylinder with cylindrical inclusions

2021· article· en· W3206411144 on OpenAlexaff
John A. Fawcett

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsCylinderScatteringMonte Carlo methodPhysicsSpeed of soundSpectral lineReflection (computer programming)OpticsField (mathematics)Realization (probability)Computational physicsGeometryMathematicsAcousticsQuantum mechanics

Abstract

fetched live from OpenAlex

In this paper, the scattering from a fluid-filled (infinite length) cylinder is considered. This cylinder, C, has a different interior sound speed and density than the surrounding water. Within the cylinder's interior, there are a number of smaller cylinders, inclusions, with yet other sound speeds and densities. The mean coherent field scattered from C is computed using Monte Carlo simulations with respect to the random realizations of the inclusion positions and compared to the results computed using an effective sound speed for C. An original formula for the effective sound speed is derived by equating the reflection coefficient for C (without inclusions) to the expected coherent scattered field from C with inclusions, assuming a single-scattering approximation. A single realization of inclusions is also considered with the backscattered spectra averaged azimuthally over the angle of the source/receiver pair. This result is then compared to the coherent fields predicted by the effective medium theory. This is performed for both spectra and the computed time series.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.014
GPT teacher head0.265
Teacher spread0.251 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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