MétaCan
Menu
Back to cohort
Record W2913947100 · doi:10.1080/02670836.2019.1567030

Banded microstructure in a low-alloyed eutectoid steel

2019· article· en· W2913947100 on OpenAlexaff
Rangasayee Kannan, Yiyu Wang, Leijun Li

Bibliographic record

VenueMaterials Science and Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBainiteMaterials scienceMicrostructureMetallurgyAusteniteMartensiteEutectic systemIntergranular corrosionGrain boundary

Abstract

fetched live from OpenAlex

The possible influence of banded microstructure on the fracture performance of a high-carbon steel is investigated using electron microscopy and X-ray diffraction techniques. The banded microstructure was found to be alternating layers of bainite and tempered martensite/retained austenite. Transformation of bainite was promoted by a combined effect of segregation of alloying elements, non-uniform thermal gradient across the steel, and the insufficient austenitisation. The transformation of bainite along the prior austenite grain boundaries (PAGBs) introduces a non-uniform strain distribution at bainite/martensite interface and a higher stress concentration, which may eventually lead to the failure of the heavy section in a brittle intergranular manner. Results provide insights to the importance of understanding the bainitic transformation in such eutectoid steel systems.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

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)0.0010.000

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 head0.003
GPT teacher head0.177
Teacher spread0.174 · 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 designObservational
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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueMaterials Science and TechnologySame topicMicrostructure and Mechanical Properties of SteelsFrench-language works237,207