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Record W2507082625 · doi:10.1002/9781118935743.ch23

Reentrant Phenomena in Relaxors

2016· other· en· W2507082625 on OpenAlexaff
Alexei A. Bokov, Zuo‐Guang Ye

Bibliographic record

Venuenot available
Typeother
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsReentrancyCondensed matter physicsSpin glassMaterials scienceMesoscopic physicsRelaxation (psychology)Phase transitionPhase (matter)FerromagnetismPhysics

Abstract

fetched live from OpenAlex

This chapter provides a comprehensive review on the reentrant relaxors, that is the materials in which both types of reentrant and relaxor behaviors are observed. The reentrant phenomena were studied elaborately in dilute magnets, where the sequence of phases from paramagnetic to ferro-, or antiferro-, magnetic and further to reentrant spin glass was observed within a particular concentration range of magnetic atoms. Relaxor ferroelectrics are often considered as an electric analog of magnetic spin glasses because they exhibit similar features, such as a quenched disorder in the structure, frustrated interactions, ultra-slow relaxation dynamics, phase transition to a glassy phase under conditions far from equilibrium, aging, rejuvenation and memory effects. The analogy between the reentrant spin glasses and relaxor ferroelectrics is not very surprising, as both are characterized by partially frustrated interactions and both exhibit a mesoscopic or even macroscopic collinear order that exists alongside a glassy order.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.250
Teacher spread0.240 · 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

Citations20
Published2016
Admission routes1
Has abstractyes

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Same topicMultiferroics and related materialsFrench-language works237,207