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Record W1970671876 · doi:10.1088/0004-637x/775/1/69

STELLAR DYNAMOS AND CYCLES FROM NUMERICAL SIMULATIONS OF CONVECTION

2013· article· en· W1970671876 on OpenAlexaff
Caroline Dubé, Paul Charbonneau

Bibliographic record

VenueThe Astrophysical Journal · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSolar and Space Plasma Dynamics
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPhysicsDynamoConvectionAstrophysicsDynamo theoryComputational astrophysicsAstronomyStellar windMechanicsStarsClassical mechanicsMagnetic field

Abstract

fetched live from OpenAlex

We present a series of kinematic axisymmetric mean-field αΩ dynamo models applicable to solar-type stars, for 20 distinct combinations of rotation rates and luminosities. The internal differential rotation and kinetic helicity profiles required to calculate source terms in these dynamo models are extracted from a corresponding series of global three-dimensional hydrodynamical simulations of solar/stellar convection, so that the resulting dynamo models end up involving only one free parameter, namely, the turbulent magnetic diffusivity in the convecting layers. Even though the αΩ dynamo solutions exhibit a broad range of morphologies, and sometimes even double cycles, these models manage to reproduce relatively well the observationally inferred relationship between cycle period and rotation rate. On the other hand, they fail in capturing the observed increase of magnetic activity levels with rotation rate. This failure is due to our use of a simple algebraic α-quenching formula as the sole amplitude-limiting nonlinearity. This suggests that α-quenching is not the primary mechanism setting the amplitude of stellar magnetic cycles, with magnetic reaction on large-scale flows emerging as the more likely candidate. This inference is coherent with analyses of various recent global magnetohydrodynamical simulations of solar/stellar convection.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.514
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.005
GPT teacher head0.214
Teacher spread0.209 · 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.

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

Citations26
Published2013
Admission routes1
Has abstractyes

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