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Record W2006803049 · doi:10.1063/1.1522380

Asymptotic analysis of a surface-interfacial wave interaction

2002· article· en· W2006803049 on OpenAlexaff
Mirmosadegh Jamali, Brian Seymour, Gregory A. Lawrence

Bibliographic record

VenuePhysics of Fluids · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhysicsWavelengthSurface waveMechanicsAsymptotic analysisPerturbation theory (quantum mechanics)Classical mechanicsSurface (topology)Perturbation (astronomy)Mathematical analysisOpticsGeometryQuantum mechanics

Abstract

fetched live from OpenAlex

The three-dimensional interaction of a surface wave with two oblique interfacial waves in a horizontally infinite two-layer fluid is analyzed asymptotically. The nondimensional density difference is taken as a perturbation parameter and simple expressions for the growth rates and kinematic properties of the waves are obtained. The results show that the interfacial wavelengths are an order smaller than the surface wavelength. Also, to the leading-order approximation, the interfacial waves have a frequency half that of the surface wave, and their directions differ by 180° in the horizontal plane. The interaction coefficients are found to be equal at the leading order. The asymptotic solution is compared with the exact solution, and an excellent agreement is obtained for the range of applicability of the asymptotic theory. The analysis is extended to interactions in a medium with sidewalls. A previous laboratory flume study is addressed, and the asymptotic theory is used to explain the experimental observations.

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)0.003
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 score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.033
GPT teacher head0.228
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

Citations29
Published2002
Admission routes1
Has abstractyes

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