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Record W2328179264 · doi:10.4164/sptj.44.242

Effect of Slurry Preparation Conditions of Soft Ferrite Powder on the Properties of Its Sintered Body

2007· article· en· W2328179264 on OpenAlexaff
Masayasu Uemura, Kazuyoshi Sato, Hiroya Abe, Makio Naito, Kiyoshi Nogi, Keizo Uematsu

Bibliographic record

VenueJournal of the Society of Powder Technology Japan · 2007
Typearticle
Languageen
FieldMaterials Science
TopicAdvanced ceramic materials synthesis
Canadian institutionsNexen (Canada)
Fundersnot available
KeywordsMaterials scienceSlurryGranule (geology)Composite materialCompressive strengthCompactionAcrylic acidVinyl alcoholAqueous solutionFerrite (magnet)Green bodyMicrostructureSinteringChemistryPolymerOrganic chemistry

Abstract

fetched live from OpenAlex

Effect of Mn-Zn slurry preparation conditions on the properties of its sintered body was examined for a granule compaction process. Poly-vinyl alcohol (PVA) binder or poly-acrylic-acid (PAA) binder was used to prepare the aqueous slurry of Mn-Zn oxides powder. The granules prepared with the acrylic binder resulted in a higher density, fracture strength and saturation magnetic flux density of the sintered body. The granules with PVA had a higher compressive strength, and formed a sintered body with a larger pore and a lower density. A thick segregated binder layer was observed on the surface of granules when PVA was used, which contributed to the higher strength of the green body compared to the granules prepared with PAA binder.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.002
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.273
Teacher spread0.261 · 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.

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

Citations0
Published2007
Admission routes1
Has abstractyes

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