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Record W108824002

SOFT MAGNETIC Fe-BASED METALLIC GLASSES PREPARED BY FLUXING AND WATER-QUENCHING

2008· article· en· W108824002 on OpenAlexaff
Tokujiro Yamamoto, Noriharu Yodoshi, Teruo Bitoh, Akihiro Makino, Akihisa Inoue

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMetallic Glasses and Amorphous Alloys
Canadian institutionsHatch (Canada)
Fundersnot available
KeywordsMaterials scienceNucleationCoercivityQuenching (fluorescence)Phase (matter)MetalAmorphous metalMetallurgyCastingMoldComposite materialThermodynamicsAlloyCondensed matter physicsOptics
DOInot available

Abstract

fetched live from OpenAlex

Fe 0.5 Co 0.5 ) 0.75 B 0.20 Si 0.05 ) 96 Nb 4 soft magnetic bulk metallic glasses were prepared by flux- ing and water-quenching in a silica tube. Dimension of the bulk metallic glass specimens was up to 7.7 mm in diameter, which is about 1.5 times larger than those prepared by Cu mold-casting. The critical cooling rate of ((Fe 0.5 Co 0.5 ) 0.75 B 0.20 Si 0.05 ) 96 Nb 4 alloys with fluxing for forming a metallic glass phase was 150 - 170 K/s, which was considerably smaller than that without fluxing. Satura- tion magnetization was 1.13 T, and coercivity was lower than 20 A/m. Fluxing suppresses hetero- geneous nucleation by isolating the nucleation sites from the molten alloys and improves their glass-forming ability.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.484
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

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 head0.009
GPT teacher head0.184
Teacher spread0.174 · 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

Citations8
Published2008
Admission routes1
Has abstractyes

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