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Record W2555419829 · doi:10.1109/jsen.2016.2631541

Pulsed Eddy Current Inspection of Wall Loss in Steam Generator Trefoil Broach Supports

2016· article· en· W2555419829 on OpenAlexafffund
Sarah G. Mokros, P. R. Underhill, Jordan Morelli, Thomas W. Krause

Bibliographic record

VenueIEEE Sensors Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsQueen's UniversityRoyal Military College of Canada
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Nuclear Laboratories
KeywordsEddy-current testingEddy currentMaterials scienceAcousticsLift (data mining)Structural engineeringMechanicsEngineeringComposite materialElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Inspection for corrosion in steam generator (SG) tube support structures is a significant component of monitoring SG condition, since support degradation can lead to SG tube flaws, and thereby further SG deterioration. A pulsed eddy current probe was developed to augment SG inspection at carbon steel trefoil broach supports. The probe demonstrated capability to measure far side wall loss as small as 20% of the original 2.7-mm-thick broach support ligament from within the Alloy 800 SG tube. A power law fit of the data was found to describe signal variation at later times under conditions of varying wall thickness and lift-off. The associated power law exponent varied linearly with wall thickness and was observed to be largely independent of lift-off and angular orientation of the probe, identifying it as a potential parameter for ligament wall thickness monitoring. Voltage response integrated over time also provided a low-noise means of measuring the wall loss when lift-off variation and misalignment were not present.

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)0.001
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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.015
GPT teacher head0.250
Teacher spread0.234 · 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

Citations17
Published2016
Admission routes2
Has abstractyes

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