MétaCan
Menu
Back to cohort

Novel Approach to Model Static Recrystallization of Austenite during Hot-Rolling of Nb-Microalloyed Steel: Effect of Precipitates

2013· article· en· W1994096858 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

VenueMaterials science forum · 2013
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsMcMaster University
Fundersnot available
KeywordsRecrystallization (geology)Materials scienceNucleationNiobiumMetallurgyAusteniteMicroalloyed steelPrecipitationDynamic recrystallizationThermomechanical processingHot workingMicrostructureThermodynamics

Abstract

fetched live from OpenAlex

Microalloying additions are critical for grain size control during thermo-mechanical processing. The addition of niobium is known to delay the onset and growth of recrystallization. A physically-based model for the interaction of strain-induced precipitation, recovery and recrystallization is presented. A key feature of the model is the incorporation of the effect of precipitation on the nucleation of recrystallization. Quantitative agreement between the experimental measurements and the model predictions has also been demonstrated. The model offers valuable insight into the relative contributions of solute and precipitate Nb as well as the optimum conditions for strain accumulation.

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)0.001
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 score0.178
Threshold uncertainty score0.597

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 communication0.0000.001
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 head0.009
GPT teacher head0.210
Teacher spread0.201 · 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