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Record W228027758 · doi:10.1002/srin.200606443

Transformation Textures in As‐hot rolled TRIP Steels

2006· article· en· W228027758 on OpenAlexafffund
John J. Jonas, Youliang He, Stéphane Godet

Bibliographic record

Venuesteel research international · 2006
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAusteniteBainiteRecrystallization (geology)Materials scienceBrassMetallurgyEquiaxed crystalsCopperTRIP steelMicrostructureGeology

Abstract

fetched live from OpenAlex

The microstructures of TRIP steels finish‐rolled above and below the recrystallization‐stop temperature (T nr ) are compared. Here, the retained austenite grains are equiaxed or elongated, respectively, according to whether final rolling was carried out above or below the T nr . The recrystallized austenite did not contain a sharp texture, the best defined component of which was the cube. The bainite that formed in this case was characterized by weak concentrations of the Goss and rotated Goss and a fairly strong concentration of the rotated cube. It also displayed the transformation products of a retained rolling fibre in the prior austenite. The deformed austenite contained the typical fcc rolling texture, where the copper is considerably more intense than the brass under these conditions. After transformation to bainite, the presence of a strong transformed copper component is evident, together with somewhat less intense contributions from the three transformed brass components. The data indicate that strong variant selection took place in the deformed austenite and that it was also present in the recrystallized material, but to a lesser extent. The latter displayed evidence of incomplete recrystallization in that the transformation texture included components obtained from both “recrystallized” and “deformed” austenite.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.926

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.027
GPT teacher head0.310
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 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

Citations4
Published2006
Admission routes2
Has abstractyes

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