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Record W2044958467 · doi:10.1121/1.4788082

Frequency dependence of elliptical particle motion of acoustic waves transmitted into the seabed from a point source in water

2005· article· en· W2044958467 on OpenAlexaff
David M. F. Chapman, Paul C. Hines, John C. Osler

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsEllipsePerpendicularAmplitudePhysicsSeabedAcousticsParticle displacementPoint sourceParticle velocityMagnetosphere particle motionDisplacement (psychology)Phase (matter)GeologyOpticsGeometryMagnetic fieldMathematics

Abstract

fetched live from OpenAlex

When perpendicular components of the particle velocity of a continuous acoustic wave have unequal amplitude and phase, the displacement traces an elliptical path. This is caused by inhomogeneous waves and/or losses in the medium. For spherical waves radiating from a point source in water and transmitted into the seabed, the orientation of the elliptical orbit varies with receiver location and frequency, and the ellipse major axis is not always parallel to the direction of propagation suggested by Snell’s law. If not accounted for, this could bias estimates of sediment sound speed derived from particle velocity measurements. This presentation describes a simple orbit model based on numerical evaluation of integrals for the transmitted field over the entire wavenumber range. The model provides rapid simulation of results for different experimental geometries and source frequencies. The model is validated using an established full-field model and compared with experimental data. [Work supported in part by ONR Code 32.]

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 communication0.0000.000
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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicUnderwater Acoustics ResearchFrench-language works237,207