MétaCan
Menu
← Back to cohort

Centrifugal Instability of a Geostrophic Jet

2020· article· en· W3084358836 on OpenAlexaff
Francis Poulin, Matthew Harris, Kevin G. Lamb

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsBaroclinityBarotropic fluidInstabilityPhysicsMechanicsNonlinear systemGeostrophic windVorticityHydrostatic equilibriumJet (fluid)Classical mechanicsVortexQuantum mechanics

Abstract

fetched live from OpenAlex

Oceanic and Atmospheric jets with sufficiently strong anticyclonic vorticity are subject to centrifugal instabilities. This mechanism is relatively fast in comparison to barotropic and baroclinic instabilities and require non-conservative forces that mix the fluid properties. In this work, we present a novel approach to compute the linear stability characteristics of both barotropic and baroclinic jets. This enables us to compute the growth rates and spatial structures very accurately and efficiently. Subsequently, by integrating the fully nonlinear, non-hydrostatic dynamics using the spectrally accurate numerical model SPINS, we validate the predictions of the linear theory and then investigate the nonlinear equilibration that results. Depending on the Reynolds number of the flows, there are instances where a secondary instability occurs that eventually produces vortical structures, some of which are themselves subject to centrifugal instabilities. This idealized investigation quantifies the effects of centrifugal instabilities as an initial step to determine how to parameterize them.

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)0.000
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.041
GPT teacher head0.215
Teacher spread0.175 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicMeteorological Phenomena and Simulations→French-language works237,207→