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Record W4246677349 · doi:10.1109/intmag.2005.1463950

An iterative method to obtain non-uniform field distribution in magnetic substrates

2005· article· en· W4246677349 on OpenAlexaff
A.R.V. Farahani, Alexander Konrad

Bibliographic record

VenueINTERMAG Asia 2005. Digests of the IEEE International Magnetics Conference, 2005. · 2005
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDemagnetizing fieldMagnetizationField (mathematics)Magnetic fieldCondensed matter physicsPhysicsMagnetostaticsComputational physicsMathematicsQuantum mechanics

Abstract

fetched live from OpenAlex

An iterative method to find the non-uniform field distribution for arbitrary shapes is studied. In this method, when a magnetic material is placed in an applied field H/sub a/ produced by an external source, interaction occurs between the applied field and the magnetic material. Initially, with no demagnetizing field, the internal field equals the applied field until the material becomes saturated in the direction of the local applied field when the field is sufficiently strong. The main iteration loop starts with this initial value for the magnetization M. The material produces a contribution, a demagnetizing field H/sub d/, to the magnetic field in which when added to the applied magnetic field, results in the total internal field H/sub i/. The magnetic material now responds to this new total field and the magnetization redistributes itself in the new direction of the local total field. The iteration continues with this new magnetization, producing new demagnetizing and total internal fields. Results show that after further iterations the field becomes more parallel and the distribution of H/sub i/ is found to be symmetric with respect to the corners. The flux originates in the upper half and ends in the lower half following the general direction of H/sub a/.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.299
Teacher spread0.283 · 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 designBench or experimental
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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

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