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Record W1982056031 · doi:10.1103/physrevb.76.235112

Metanematic transitions in a bilayer system: Application to the bilayer ruthenate

2007· article· en· W1982056031 on OpenAlexafffund
Christoph M. Puetter, Hyeonjin Doh, Hae‐Young Kee

Bibliographic record

VenuePhysical Review B · 2007
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaAlfred P. Sloan Foundation
KeywordsLiquid crystalCondensed matter physicsBilayerAnisotropyMaterials sciencePhase (matter)Magnetic fieldPhase transitionPhysicsChemistryOptics

Abstract

fetched live from OpenAlex

It was suggested that the two consecutive metamagnetic transitions and the large residual resistivity discovered in ${\mathrm{Sr}}_{3}{\mathrm{Ru}}_{2}{\mathrm{O}}_{7}$ can be understood via the nematic order and its domains in a single layer system. However, a recently reported anisotropy between two longitudinal resistivities induced by tilting the magnetic field away from the $c$ axis cannot be explained within the single layer nematic picture. To fill the gap in our understanding within the nematic order scenario, we investigate the effects of bilayer coupling and in-plane magnetic field on the electronic nematic phases in a bilayer system. We propose that the in-plane magnetic field in the bilayer system modifies the energetics of domain formation, since it breaks the degeneracy of two different nematic orientations. Thus, the system reveals a pure nematic phase with a resistivity anisotropy in the presence of an in-plane magnetic field. In addition to the nematic phase, the bilayer coupling opens a different route to a hidden nematic phase that preserves the $x\text{\ensuremath{-}}y$ symmetry of the Fermi surfaces.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 head0.013
GPT teacher head0.297
Teacher spread0.284 · 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 designTheoretical or conceptual
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

Citations40
Published2007
Admission routes2
Has abstractyes

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