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Record W2301052015 · doi:10.1109/tmag.2015.2498910

Magnetization of Linear Arrays of Two Ferromagnetic Spheres in a Uniform Magnetic Field

2015· article· en· W2301052015 on OpenAlexafffund
Gehan Anthonys

Bibliographic record

VenueIEEE Transactions on Magnetics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLaplace's equationPhysicsMagnetizationMagnetic fieldScalar (mathematics)SPHERESVector LaplacianScalar potentialSpherical coordinate systemSuperposition principleMagnetic potentialLaplace operatorCylindrical coordinate systemMagnetic fluxBoundary value problemClassical mechanicsMagnetic domainMathematical analysisVector potentialMathematicsMechanicsQuantum mechanicsGeometry

Abstract

fetched live from OpenAlex

The main objective of this paper is to explore the exact analytical solutions of the magnetization of linear arrays of two ideal ferromagnetic spheres in the presence of external magnetic fields. First, the total scalar magnetic potential outside the spheres, related to the magnetic field intensity, which satisfies the Laplace equation, is obtained by the superposition of the potentials due to all spheres and the potential corresponding to the external field. The translational addition theorems for scalar Laplacian functions in spherical coordinates are then used to translate the two coordinates systems into one coordinates system. Then, the exact boundary conditions were used to solve the magnetic field quantities outside the system. On the other hand, the scalar magnetic potential inside each sphere, related to the magnetic flux density, also satisfies the Laplace equation, which is solved by imposing the boundary conditions known from the solution of the outside quantities. Finally, the expressions derived are used to generate numerical results of controllable accuracy for various field quantities.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
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 studies0.0000.000
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 head0.015
GPT teacher head0.257
Teacher spread0.242 · 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

Citations1
Published2015
Admission routes2
Has abstractyes

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