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Record W2123258030 · doi:10.1109/tmag.2013.2283659

Kinetic Monte Carlo Simulations of $M\!\!-\!\!H$ Loops for HAMR Recording Media: Comparison With MOKE Data

2013· article· en· W2123258030 on OpenAlexaff
M. L. Plumer, T. J. Fal, J. I. Mercer, J. P. Whitehead, J. van Ek, Antony Ajan

Bibliographic record

VenueIEEE Transactions on Magnetics · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsMemorial University of Newfoundland
FundersWestern Digital
KeywordsMonte Carlo methodAnisotropyCondensed matter physicsHeat-assisted magnetic recordingMicromagneticsSaturation (graph theory)Computational physicsPatterned mediaPhysicsKinetic energyMagnetizationKinetic Monte CarloMaterials scienceMagnetic anisotropyKerr effectNuclear magnetic resonanceOpticsMagnetic fieldAcousticsClassical mechanics

Abstract

fetched live from OpenAlex

Our previously developed kinetic Monte Carlo algorithm is used to simulate M-H loops of high anisotropy magnetic recording media at long time scales relevant to the experimental measurements using the magneto-optic Kerr effect. Micromagnetic parameters are fit to loop data taken at 300 K and at a sweep rate of 700 Oe/s on a single-layer media developed for heat-assisted magnetic recording. Significantly different fitted parameters result from standard micromagnetic simulations that can access only sweep rates many orders of magnitude faster. Sensitivities of the loops to anisotropy, saturation magnetization, and various distributions are reported.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.782
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.042
GPT teacher head0.263
Teacher spread0.221 · 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

Citations4
Published2013
Admission routes1
Has abstractyes

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