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Record W2766657846 · doi:10.5151/1983-4764-30399

ADVANCES IN THE ASSESSMENT OF HOT CRACKING OF Cu-CONTAINING STEELS

2017· article· en· W2766657846 on OpenAlexafffund
O. Comineli, Clodualdo Aranas, Rian Dippenaar

Bibliographic record

VenueABM Proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcGill University
FundersOulun YliopistoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorMcGill University
KeywordsCrackingDuctility (Earth science)Materials scienceMetallurgySchematicUltimate tensile strengthHot spot (computer programming)Tensile testingHot rolledComposite materialEngineeringComputer scienceCreep

Abstract

fetched live from OpenAlex

While the hot cracking of Cu-containing steels is a serious and widely known problem for the industry, the literature reports that copper only slightly impairs the hot-ductility measured in laboratory. A distinction is drawn between hot ductility and hot shortness and the respective cracking operating mechanism involved at the respective range of temperature. The representability of the laboratory assessment of hot cracking of Cucontaining steels by hot tensile tests compared with actual results in industrial practice is discussed. The effectiveness of the variable affecting in each cracking mechanism and respective temperature range, focused in improving the hot cracking assessment of Cu-steels and the relative importance of oxidation will also be discussed. It is concluded that the hot tensile test in argon might be a good simulation if properly interpreted and that the role of oxidation is secondary in causing hot shortness. A schematic diagram for the hot tensile test is proposed.

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)0.002
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.290
Teacher spread0.274 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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