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Record W2286880570 · doi:10.1088/0953-8984/28/6/066001

Inter and intra macro-cell model for point dipole–dipole energy calculations

2016· article· en· W2286880570 on OpenAlexfundno aff
G J Bowden, Gavin B. G. Stenning, G. van der Laan

Bibliographic record

VenueJournal of Physics Condensed Matter · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsnot available
FundersUniversity of SouthamptonYork University
KeywordsDipolePhysicsMagnetic dipoleMacroDiscrete dipole approximationPoint (geometry)Range (aeronautics)Electric dipole transitionEnergy (signal processing)MicromagneticsField (mathematics)Computational physicsMagnetic fieldMaterials scienceComputer scienceGeometryQuantum mechanicsMathematicsMagnetization

Abstract

fetched live from OpenAlex

In the field of micromagnetics, the calculation of long-range dipole-dipole interactions in non-uniformly magnetized bodies has long posed computational problems. In this paper, we present an inter and intra macro-cell point-dipole model, which can be used to speed-up the determination of dipole-dipole energies at the atomistic level. The model can be used to accurately compute the dipole-dipole energy, using macro-cells of any shape or size.

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)0.001
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: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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
Published2016
Admission routes1
Has abstractyes

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