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Record W2803769504 · doi:10.1139/cjc-2018-0257

Retraction: Effects of Li-doping on microstructural and electrical properties of ZnO–MgO–Al<sub>2</sub>O<sub>3</sub> linear resistance ceramics

2018· article· en· W2803769504 on OpenAlexvenueno aff
Rong Yang, Xiao Qu, Maohua Wang

Post-publication record

OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.

Bibliographic record

VenueCanadian Journal of Chemistry · 2018
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsnot available
Fundersnot available
KeywordsCeramicMicrostructureElectrical resistivity and conductivityTemperature coefficientDopingAnalytical Chemistry (journal)Scanning electron microscopeMaterials scienceDiffractionActivation energyMineralogyChemistryComposite materialOpticsPhysical chemistryOptoelectronicsElectrical engineering

Abstract

fetched live from OpenAlex

In this article, The ZnO-MgO-Al2O3 linear resistance ceramics doped with Li2O have been prepared by the conventional ceramic method. The microstructure and the crystal characteristics were investigated by field emission scanning electron microscopy (FESEM) equipped with energy dispersive spectrometer (EDS) and X-ray diffraction (XRD) respectively. With the increasing of Li2O content, the grain growth of ZnO-based linear resistance ceramics was enhanced and the resistivity was also influenced significantly. The experimental result indicates that the doping of Li2O can reduces the nonlinear coefficient and improves the resistance temperature coefficient of the ZnO-based linear resistance ceramics obviously. The samples at the Li2O content of 8 mol% exhibit excellent electrical properties with the resistivity of 589.34 Ω cm and the resistance temperature coefficient of 0.8 *10-4/ oC. Moreover, the nonlinear coefficient of the ZnO-based linear resistance ceramics decreases from 1.28 to 1.14 as the Li2O concen...

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Other
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
gptno category
Domain: not available · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.996
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0160.012

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther · Editorial

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

Citations0
Published2018
Admission routes1
Has abstractyes

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