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Record W2565671888 · doi:10.5539/jmsr.v6n1p24

Acicular Ferrite Formation and Its Influencing Factors-A Review

2016· article· en· W2565671888 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Materials Science Research · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsnot available
Fundersnot available
KeywordsAcicular ferriteMaterials scienceNucleationBainiteMetallurgyAcicularFerrite (magnet)MicrostructureAusteniteToughnessComposite material

Abstract

fetched live from OpenAlex

Acicular ferrite is a microstructure nucleating intergranularly on non-metallic inclusions and forming an arrangement of fine, interlocking grains. This structure is known to improve steel properties, especially steel toughness, essentially. The formation of acicular ferrite is mainly affected by steel composition, cooling rate, inclusion landscape and austenite grain size. In recent decades, extensive research has been conducted to investigate these factors. The present paper provides an overview of the impact of published results and the state of knowledge regarding acicular ferrite formation. Special attention is paid to the effect of carbon, manganese and titanium addition to steel, as well as the optimum size, number and composition of non-metallic inclusions. In addition, the reactions during the nucleation and growth of acicular ferrite needles are briefly addressed. Further, characteristics of acicular ferrite and bainite are summarized, which should help to distinguish these similar structures.

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.

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)0.004
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 score0.007
Threshold uncertainty score0.131

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.077
GPT teacher head0.340
Teacher spread0.264 · 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