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Record W3007607567 · doi:10.1029/2020jc016120

Breaking of Internal Kelvin Waves Shoaling on a Slope

2020· article· en· W3007607567 on OpenAlexaff
Keisuke Nakayama, T. Sato, Kojiro Tani, Leon Boegman, Ichiro FUJITA, Tetsuya SHINTANI

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsQueen's University
FundersJapan Society for the Promotion of Science
KeywordsKelvin waveInternal waveBreaking waveGeologyMechanicsShoalJet (fluid)Particle image velocimetryShoaling and schoolingPhysicsGeophysicsTurbulenceWave propagationOceanography

Abstract

fetched live from OpenAlex

Abstract In stratified flow, breaking of internal waves over slopes induces resuspension of bottom sediments and transport of mass. When internal waves shoal and break, flow dynamics and mass transport differ significantly according to whether the Coriolis force is included or neglected. Despite its importance, the currents generated by breaking internal Kelvin waves remain uninvestigated. Therefore, this study considers breaking of internal waves over a uniform slope under Coriolis with equivalent upper‐ and lower‐layer depths. Laboratory experiments, using a 6.0‐m rotating tank, were undertaken to visualize currents using particle image velocimetry. Experimental data validated a three‐dimensional fluid dynamics model, in which a phase‐averaged velocity (residual jet) was simulated to occur at the lateral wall (to the right) of the progressive internal Kelvin waves in the breaking zone, with the generation of an oblique downslope return flow (downdraft) under Coriolis. The geostrophic balance drove the residual jet, and the equation for estimating the residual current, due to the jet, was formulated and was discussed by referring a coastal jet in Lake Erie. The results provide insight on mass transport in lakeshore and coastal zones.

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)0.001
metaresearch head score (Gemma)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
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 head0.076
GPT teacher head0.325
Teacher spread0.250 · 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 designObservational
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

Citations19
Published2020
Admission routes1
Has abstractyes

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