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Neutron Laue Diffraction Study on the Magnetic Phase Diagram of Multiferroic<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>MnWO</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math>under Pulsed High Magnetic Fields

2011· article· lv· W2034864333 on OpenAlexafffund
Hiroyuki Nojiri, Shunsuke Yoshii, Masaaki Yasui, Keisuke Okada, Masaaki Matsuda, Jong-Suck Jung, T. Kimura, Louis J. Santodonato, G. E. Granroth, K. A. Ross, J. P. Carlo, B. D. Gaulin

Bibliographic record

VenuePhysical Review Letters · 2011
Typearticle
Languagelv
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsMcMaster University
FundersOak Ridge National LaboratoryBasic Energy SciencesNatural Sciences and Engineering Research Council of CanadaMcMaster UniversityU.S. Department of Energy
KeywordsNeutron diffractionNeutronSpallation Neutron SourceNeutron scatteringSpallationBragg's lawMagnetic fieldDiffractionMultiferroicsPhase diagramPhase (matter)PhysicsMaterials scienceOpticsMagnetic structureCondensed matter physicsNuclear physicsMagnetizationOptoelectronicsFerroelectricity

Abstract

fetched live from OpenAlex

We have combined time-of-flight neutron Laue diffraction and pulsed high magnetic fields at the Spallation Neutron Source to study the phase diagram of the multiferroic material MnWO(4). The control of the field-pulse timing enabled an exploration of magnetic Bragg scattering through the time dependence of both the neutron wavelength and the pulsed magnetic field. This allowed us to observe several magnetic Bragg peaks in different field-induced phases of MnWO(4) with a single instrument configuration. These phases were not previously amenable to neutron diffraction studies due to the large fields involved.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.275
Teacher spread0.246 · 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 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

Citations55
Published2011
Admission routes2
Has abstractyes

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