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Record W2321792795 · doi:10.1080/21663831.2016.1164259

Lead-induced stress corrosion cracking behavior of mechanically surface-treated alloy 690

2016· article· en· W2321792795 on OpenAlexafffund
Nana Li, San‐Qiang Shi, Jing‐Li Luo, Jian Lü, Ning Wang

Bibliographic record

VenueMaterials Research Letters · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsUniversity of Alberta
FundersHong Kong Polytechnic UniversityNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsMaterials scienceCorrosionMetallurgyStress corrosion crackingAlloyCrackingStress (linguistics)ImpurityComposite material

Abstract

fetched live from OpenAlex

Lead (Pb) is one of the impurities that cause stress corrosion cracking (SCC) of the tubing material in the steam generator (SG), the corrosion behavior of surface mechanical attrition treatment (SMAT) treated surface was measured for alloy 690 in simulated SG condition with and without Pb contamination. Even though the polarization curves indicated the corrosion potential of SMAT-processed specimens shifted negatively, the positive effect of SMAT on resistance of SCC was observed. The reduced susceptibility to lead-induced stress corrosion cracking may be attributed to the formation of stable passive film and the significant compressive stress on the surface induced by SMAT.Impact StatementA significant compressive stress induced by the SMAT process effectively reduced the susceptibility of the alloys to lead-induced stress corrosion cracking.

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 score0.002
Threshold uncertainty score0.004

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 head0.058
GPT teacher head0.334
Teacher spread0.276 · 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

Citations8
Published2016
Admission routes2
Has abstractyes

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