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Record W113000861 · doi:10.5006/c2006-06600

Novel Approach to Life Extension of Components in BWR

2006· article· en· W113000861 on OpenAlexaff
Young‐Jin Kim, Peter L. Andresen, Catherine P. Dulke, James Burner

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsResearch & Development Corporation
Fundersnot available
KeywordsLife extensionExtension (predicate logic)Computer scienceReliability engineeringForensic engineeringProgramming languageEngineering

Abstract

fetched live from OpenAlex

Abstract The fundamental understanding and practical application for mitigating degradation of structural materials in boiling water reactor (BWR) is described. Controlling the electrochemical property of surface alters the electrochemical corrosion potential (ECP) of structural materials and subsequently affects the stress corrosion cracking susceptibility in high temperature water. It is evident that the presence of noble metals on the oxide surface dramatically improves the catalytic recombination efficiency of hydrogen (H2) to oxygen (O2) and hydrogen peroxide (H2O2) to form water (H2O), and thus results in a thermodynamically lowest ECP value when a stoichiometric or higher amount of hydrogen is present in the water. It is also observed that an protective insulating coating (PIC) layer created with powders of yttria-stabilized zirconia (YSZ), pure zirconium (Zr) or zirconium alloys by thermal spray, chemical vapor deposition (CVD), or physical vapor deposition (PVD) restricts the oxidant transport rate to the metal surface, and decreases the ECP in high temperature water containing high concentration of oxidants without addition or presence of H2. In addition, the longer lifetime of the ECP sensor was achieved by applying the thermal spraying coating with YSZ powders. Furthermore, surface treatment of the hard friction surface of steam line plug grips with a Ta2O5 coating improves the corrosion resistance of hard-friction coated aluminum steam line plug grips.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.040
GPT teacher head0.230
Teacher spread0.190 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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