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Record W3200763722 · doi:10.1063/5.0064964

Mode-one internal tides propagating across a geostrophic current

2021· article· en· W3200763722 on OpenAlexafffund
Yangxin He, Kevin G. Lamb

Bibliographic record

VenuePhysics of Fluids · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsBaroclinityBarotropic fluidCurrent (fluid)Thermal windMechanicsGeostrophic windReflection (computer programming)Nonlinear systemInternal waveShear (geology)Classical mechanicsComputational physicsGeophysicsMeteorologyWind shearWind speedGeologyThermodynamics

Abstract

fetched live from OpenAlex

As a mode-1 internal tide (IT) propagates across a geostrophic current V(x, z), we have investigated the amount of IT energy reflected from the current and the impact of the current on the transmitted wave field. These are quantified by considering the reflection coefficient R and the linear modal energy conversion Pn, where n is the modal number. Here, a linear theory built upon idealized barotropic currents is presented. Fully nonlinear numerical simulations are used for the baroclinic currents. We conclude that the reflection is determined by the horizontal shear of the current Vx through varying the effective frequency feff. The modal energy conversion Pn is determined by the vertical shear of the current Vz, i.e., the horizontal variation of the density ρx as a result of the thermal wind relation. The current can increase R up to around 50%. However, Pn is less than 6% among all our simulations. This indicates that IT can propagate through the current without losing much of its structure and the interaction is mostly linear.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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 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

Citations2
Published2021
Admission routes2
Has abstractyes

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