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Record W4292426835 · doi:10.1063/5.0092586

The influence of aluminium metal matrix composite reinforcement - A mini review

2022· review· en· W4292426835 on OpenAlexaff
N. E. Udoye, A.O. Inegbenebor, O.S.I. Fayomi, K. M. Oluwasegun, C. A. Ezegwu

Bibliographic record

VenueAIP conference proceedings · 2022
Typereview
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaterials scienceComposite numberComposite materialAluminiumMetal matrix compositeMatrix (chemical analysis)CastingFabricationMetalReinforcementWear resistanceMetallurgy

Abstract

fetched live from OpenAlex

The joined influence of fortifying metal matrix composites with separate and numerous particle reinforcing agents like hybrid metal matrix composites are utilized greatly in the manufacturing industry. This is attributed to the enriched mechanical and wears characteristics such as strength, impact and wear resistance. In the present study, a series of research undertakings were carried out. This paper directs the technologists and scientists towards the appropriate collection of materials by their constituents in the field of study and diverse methods used in the fabrication of metal matrix composites, especially on the metallurgical stir casting process.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.036
GPT teacher head0.278
Teacher spread0.242 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2022
Admission routes1
Has abstractyes

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