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Static Recrystallization Characteristic of Low Temperature Rolled AZ31 Magnesium Alloy during Annealing

2015· article· en· W2249830142 on OpenAlexafffund
Jing Su, Mehdi Sanjari, Abu Syed Humaun Kabir, In‐Ho Jung

Bibliographic record

VenueMaterials science forum · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRecrystallization (geology)Materials scienceAnnealing (glass)MicrostructureSofteningDynamic recrystallizationMetallurgyAlloyMagnesium alloyKineticsHot workingComposite material

Abstract

fetched live from OpenAlex

Microstructure evolution and static recrystallization kinetics of a magnesium AZ31 alloy during annealing have been investigated. The Mg alloy sheets were rolled at 100°C at a low rolling speed of 15m/min and a high rolling speed of 1000m/min to reductions of 8%, 23% and 30%. Annealing was then conducted on the as-rolled specimens at 200°C for different times. The microstructure was characterized by optical microscopy and recrystallization kinetics were evaluated by means of Johnson-Mehl-Avrami-Kolmogorov (JMAK) model. The softening behavior was found to be divided into two annealing stages. The first stage was related to the recrystallization on high stored energy regions, such as shear bands and twins, while the second stage was associated to the recrystallization of low stored energy areas.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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

Citations3
Published2015
Admission routes2
Has abstractyes

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