MétaCan
Menu
Back to cohort

Dynamic Transformation of Austenite at Temperatures above the Ae<sub>3</sub>

2018· article· en· W2905683415 on OpenAlexafffund
John J. Jonas, Clodualdo Aranas, Samuel Filgueiras Rodrigues

Bibliographic record

VenueMaterials science forum · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAusteniteMaterials scienceFerrite (magnet)SofteningTransformation (genetics)MetallurgyComposite materialThermodynamicsMicrostructure

Abstract

fetched live from OpenAlex

Under loading above the Ae3 temperature, austenite transforms displacively into Widmanstätten ferrite. Here the driving force for transformation is the net softening during the phase change while the obstacle consists of the free energy difference between austenite and ferrite as well as the work of shear accommodation and dilatation during the transformation. Once the driving force is higher than the obstacle, phase transformation occurs. This phenomenon was explored here by means of the optical and electron microscopy of a C-Mn steel deformed above their transformation temperatures. Strain-temperature-transformation (STT) curves are presented that accurately quantify the amount of dynamically formed ferrite; the kinetics of retransformation are also specified in the form of appropriate TTRT diagrams. This technique can be used to improve the models for transformation on accelerated cooling in strip and plate rolling.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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)0.0030.000

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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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 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

Citations2
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueMaterials science forumSame topicMicrostructure and Mechanical Properties of SteelsFrench-language works237,207