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Record W1978035250 · doi:10.1080/03091920802044787

Vertically sheared horizontal flow with mass sources: a canonical balanced model

2008· article· en· W1978035250 on OpenAlexaffabout
Andrew J. Majda, Abdolmajid Mohammadian, Yulong Xing

Bibliographic record

VenueGeophysical & Astrophysical Fluid Dynamics · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsGLS Industries (Canada)Environment and Climate Change Canada
Fundersnot available
KeywordsFlow (mathematics)MechanicsHorizontal and verticalGeologyMass flowGeometryPhysicsMathematics

Abstract

fetched live from OpenAlex

Abstract Canonical balanced dynamic equations involving vertically sheared horizontal flow with heat or mass sources have emerged recently in systematic multi-scale modeling of the equatorial wave guide on a wide range of spatio-temporal scales. Here, a new self-contained derivation of these equations is developed briefly in a context for potential applications to the hurricane embryo. These canonical balanced equations are studied through a combination of exact solutions and simple numerics. The results below include elementary exact solutions given by velocity fields that are linear in the spatial coordinates combined with an exact nonlinear stability analysis for vertical vorticity amplification in such a preconditioned environment. Other elementary solutions studied here include the evolution of radial eddies, which represent “hot towers” in the hurricane embryo in a suitable radial preconditioned background environment. Keywords: CyclogenesisHot towersStrong shear sourcesVertical shearHorizontal flowsStability analysis Acknowledgments The research of A. Majda is partially supported by ONR N0014-05-1-0164 and NSF DMS-0456713. The research of M. Mohammadian is supported by the National Sciences and Engineering Research Council of Canada (NSERC) through the grant PDF-329052-2006.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.101
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.185
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 teacher head, not a consensus.

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

Citations16
Published2008
Admission routes2
Has abstractyes

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