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Record W2801544461 · doi:10.1016/j.mprp.2018.04.001

Metal particle shape: A practical perspective

2018· article· en· W2801544461 on OpenAlexaff
Alireza Nouri, Antonella Sola

Bibliographic record

VenueMetal Powder Report · 2018
Typearticle
Languageen
FieldEngineering
TopicPowder Metallurgy Techniques and Materials
Canadian institutionsSmarter Alloys (Canada)
Fundersnot available
KeywordsMaterials sciencePowder metallurgyParticle (ecology)Perspective (graphical)Particle sizeMetallurgyMetalCompressibilityMetal powderComposite materialMechanicsMicrostructureChemical engineeringGeometryEngineeringMathematicsPhysics

Abstract

fetched live from OpenAlex

The choice of appropriate processing conditions in powder metallurgy and the achievement of the required properties for the final metal components greatly depend on the shape of the solid phase. Powder sinterability, packing density, compressibility, mechanical behavior, reactivity and flowability, among the others, are influenced by particle shape. The present paper offers an informative overview on the role of particle shape in powder metallurgy and provides the basic definitions, qualitative shape descriptors and semi-quantitative shape factors that are essential to better understand the effect of metal particle shape in everyday industrial practice.

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)0.002
metaresearch head score (Gemma)0.004
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: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.003

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 head0.032
GPT teacher head0.315
Teacher spread0.283 · 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
GenreOther

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

Citations50
Published2018
Admission routes1
Has abstractyes

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