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Record W4281252582 · doi:10.52843/cassyni.630y4l

Shear-instability in Internal Kelvin waves

2022· preprint· en· W4281252582 on OpenAlexaff
Marek Stastna

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOceanographic and Atmospheric Processes
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsInternal waveInstabilityStratification (seeds)Shear (geology)MechanicsTurbulencePhysicsStratified flowsVorticityGeostrophic windKelvin wavePotential vorticityInertial waveClassical mechanicsGeophysicsGeologyMechanical waveMeteorologyStratified flowWave propagationLongitudinal waveOpticsVortex

Abstract

fetched live from OpenAlex

Internal waves in the ocean and their laboratory analogues are known to break down via a variety of mechanisms. In this talk I will focus on Direct Numerical Simulations of mode-2 internal waves generated from a lock-release on a laboratory scale. This well-understood process of generation allows us to control the size and shape of the wave formed, for example through a judicious choice of density stratification. In the absence of rotation the internal waves generated, primarily induces stream wise and vertical currents, with span wise currents being the product of small scale three-dimensionalization. When rotation is present, the process of geostrophic adjustment leads to a complex pattern of wave-induced currents oriented in all three coordinate directions. I will demonstrate that this leads to shear instabilities that are trapped near one of the tank walls, it also leads to smaller scale shear instabilities with a stream wise oriented vorticity axis (to our knowledge the first documented case of such instabilities). I will discuss the breakdown into turbulence of these instabilities and the possibilities and challenges of scale up of these simulations to the field scale.

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: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.225
Teacher spread0.211 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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