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Record W2059421891 · doi:10.1115/pvp2002-1187

Experiences in Examining and Dispositioning Flaws in Zr-2.5Nb Pressure Retaining CANDU Reactor Components

2002· article· en· W2059421891 on OpenAlexaff
Andrew Celovsky

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsEnergie NB Power (Canada)Atomic Energy (Canada)
Fundersnot available
KeywordsComponent (thermodynamics)Pressure vesselNuclear engineeringMaterials scienceProcess (computing)AlloyReliability engineeringComputer scienceForensic engineeringEngineeringMetallurgyComposite materialThermodynamicsPhysics

Abstract

fetched live from OpenAlex

CANDU reactors use Zr-2.5 Nb alloy pressure tubes, as the primary pressure boundary within the reactor core. These components are subject to periodic inspection and material surveillance programs. Occasionally, the inspection program uncovers a flaw, whereupon the flaw is assessed as to whether it compromises the integrity of the pressure-retaining component. In 1998, such a flaw was observed in one pressure tube of a reactor. Non-destructive techniques and analysis were used to form a basis to disposition the flaw, and the component was fit for a limited service life. This component was eventually removed from service, whereupon the destructive examinations were used to validate the disposition assumptions used. Such a process of validation provides credibility to the disposition process. This paper reviews the original flaw and its subsequent destructive evaluation.

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)0.006
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.056
GPT teacher head0.225
Teacher spread0.170 · 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 designObservational
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

Citations1
Published2002
Admission routes1
Has abstractyes

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