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Record W305280434

Intercomparisonn of Techniques for Pressure Tube Inspection of Pressurized heavy Water Reactor

2005· article· en· W305280434 on OpenAlexaboutno aff
Hee-Jong Lee, Yong-Si Kim, Byungsik Yoon, Young‐Ho Lee

Bibliographic record

VenueJournal of the Korean Society for Nondestructive Testing · 2005
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsSizingPressurized water reactorTube (container)Eddy-current testingNuclear engineeringUltrasonic testingEddy currentUltrasonic sensorEngineeringPressure vesselWater pressureSample (material)Structural engineeringMaterials scienceMechanical engineeringMarine engineeringAcousticsElectrical engineeringChemistry
DOInot available

Abstract

fetched live from OpenAlex

This paper describes the analysis results of a series f Round-Robin test that was performed to intercompare inspection and diagnosis techniques for characterization of pressure tube f a pressurized heavy water reactor under the Coordinated Research Project(CRP) of IAEA's nuclear Power Programme. For this test, six nations, Korea, Canada, India, Argentina, Rumania, and China that currently have pressurized heavy water reactors under operation involved, and the KOR-1 pressure tube sample prepared by Korea was used. Two kinds of NDE technique, ultrasonic and eddy current test, were applied for these tests. The KOR-1 pressure tube sample contains total 12 artificial flaws such as crack-like EDM notches, wear that is similar to the real flaws and can be produced on the pressure tubes during plant operation. Test results showed that seven laboratories from six nations detected all twelve flaws in KOR-1 specimen by using ultrasonic and eddy current test methods, and ultrasonic test method was more accurate than eddy current test method in flaw detectin and sizing. ID flaws in pressure tube sample were more easily detected and accurately sized than OD flaws.

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)0.001
metaresearch head score (Gemma)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.023
GPT teacher head0.260
Teacher spread0.237 · 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

Citations0
Published2005
Admission routes1
Has abstractyes

Explore more

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