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Record W2159308750 · doi:10.1109/27.902224

An adaptive MHD method for global space weather simulations

2000· article· en· W2159308750 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Plasma Science · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsYork UniversityUniversity of Toronto
Fundersnot available
KeywordsMagnetohydrodynamicsDiscretizationDomain decomposition methodsMassively parallelFinite volume methodDiscontinuous Galerkin methodMagnetohydrodynamic driveRobustness (evolution)Computer scienceScalabilityComputational sciencePartial differential equationApplied mathematicsMultigrid methodAdaptive mesh refinementPhysicsParallel computingPlasmaFinite element methodMechanicsMathematical analysisMathematics

Abstract

fetched live from OpenAlex

A 3D parallel adaptive mesh refinement (AMR) scheme is described for solving the partial-differential equations governing ideal magnetohydrodynamic (MHD) flows. This new algorithm adopts a cell-centered upwind finite-volume discretization procedure and uses limited solution reconstruction, approximate Riemann solvers, and explicit multi-stage time stepping to solve the MHD equations in divergence form, providing a combination of high solution accuracy and computational robustness across a large range in the plasma /spl beta/ (/spl beta/ is the ratio of thermal and magnetic pressures). The data structure naturally lends itself to domain decomposition, thereby enabling efficient and scalable implementations on massively parallel supercomputers. Numerical results for MHD simulations of magnetospheric plasma flows are described to demonstrate the validity and capabilities of the approach for space weather applications.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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