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Record W2029107191 · doi:10.1115/1.4005067

Improved Wear Resistance of Dendrite Composite Eutectic Fe-B Alloy

2011· article· en· W2029107191 on OpenAlexaff
Licai Fu, Jun Yang, Qinling Bi, Weimin Liu

Bibliographic record

VenueJournal of Tribology · 2011
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDendrite (mathematics)Eutectic systemAlloyMaterials scienceComposite numberMetallurgyTribologyPhase (matter)OxideComposite materialGeometryChemistry

Abstract

fetched live from OpenAlex

The dry-sliding tribological properties of the dendrite composite eutectic Fe-B alloys (Fe94.3B5.7, Fe75B25 Fe67B33) were studied comparatively with various sliding speeds. The friction coefficient of the Fe-B alloy changes slightly with the boron content. The wear rate of the Fe94.3B5.7 alloy with about 30 vol. % dendrite t-Fe2B is only one third of Fe75B25 alloy with 15 vol. % dendrite and Fe67B33 alloy with 90 vol. % dendrite in the high sliding speed. First, a hard t-Fe2B phase reduced the wear of the Fe-B alloy directly. Second, the compactly oxide layers resulting from oxidation of the α-Fe on the worn surfaces also decreases the wear rate of Fe-B alloys. On the whole, the wear rate of the Fe94.3B5.7 is lower than Fe67B33 and Fe75B25.

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 score0.018
Threshold uncertainty score0.528

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)0.0000.000

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 head0.019
GPT teacher head0.205
Teacher spread0.186 · 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

Citations2
Published2011
Admission routes1
Has abstractyes

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