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Record W2771710048 · doi:10.31399/asm.hb.v14a.a0004029

Transformation and Recrystallization Textures Associated with Steel Processing

2005· book-chapter· en· W2771710048 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueASM International eBooks · 2005
Typebook-chapter
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcGill University
Fundersnot available
KeywordsRecrystallization (geology)AusteniteMaterials scienceMetallurgyAnnealing (glass)Dynamic recrystallizationHot workingMicrostructureGeology

Abstract

fetched live from OpenAlex

Abstract The processing of steel involves five distinct sets of texture development mechanisms, namely, austenite deformation, austenite recrystallization, gamma-to-alpha transformation, ferrite deformation, and static recrystallization during annealing after cold rolling. This article provides an introduction on crystallographic textures. It discusses the effects of austenite rolling and recrystallization on the texture and transformation behavior of recrystallized austenite and deformed austenite. The article illustrates the overall summary of the rolling and transformation behavior. It details cold-rolling textures, annealing textures, and recrystallization textures of steel samples. The article concludes with a summary of texture development during cold rolling and annealing.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score0.793

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 head0.010
GPT teacher head0.189
Teacher spread0.179 · 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