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Record W2093735966 · doi:10.1080/03091920701485537

Numerical models of zonal flow dynamos: an application to the ice giants

2007· article· en· W2093735966 on OpenAlexaff
N. Gómez Pérez, Moritz Heimpel

Bibliographic record

VenueGeophysical & Astrophysical Fluid Dynamics · 2007
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGeomagnetism and Paleomagnetism Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDynamoPhysicsMagnetic Prandtl numberMagnetic diffusivityDynamo theoryNeptuneDipoleMagnetic fieldUranusConvectionRayleigh numberSolar dynamoMechanicsAstrophysicsClassical mechanicsNatural convectionNusselt numberPlanetReynolds numberTurbulence

Abstract

fetched live from OpenAlex

The weakly dipolar and strongly tilted magnetic fields of Uranus and Neptune are apparently generated by a dynamo process distinct from that which produces axial dipoles. We study a suite of numerical dynamos driven by convection in a rapidly rotating spherical shell and focus on cases with relatively high magnetic diffusivity. Models are presented with magnetic Prandtl number Pm = 0.1–5.0 and Rayleigh numbers between 10 and 80 times the critical Rayleigh number for convection. In the cases with high magnetic diffusivity, the fluid flow has a dominant effect over the magnetic fields, which are characteristically quadrupolar and octupolar and strongly variable in time. The dipolar component is typically weak, and strongly tilted from the axis of rotation. Most of the cases we present result in low Alfvén and Elsasser numbers, in agreement with previous studies of nondipolar dynamos. Our results suggest that the peculiar magnetic fields of Uranus and Neptune result from dynamo action driven by convectively generated, strong zonal flow in the electrolytic fluid envelope.

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)0.002
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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.005
GPT teacher head0.225
Teacher spread0.221 · 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

Citations21
Published2007
Admission routes1
Has abstractyes

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