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Record W6977217062 · doi:10.6084/m9.figshare.20029451

Trending Integrated Leakage-Rate Test Results

2022· article· en· W6977217062 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2022
Typearticle
Languageen
FieldEngineering
TopicNuclear Engineering Thermal-Hydraulics
Canadian institutionsnot available
Fundersnot available
KeywordsLicenseeLeakage (economics)Test (biology)LeakHydrostatic testLeak detection

Abstract

fetched live from OpenAlex

Existing Canadian Nuclear Power Plants (NPPs) are aging, with 19 active reactors being in operation for 35 to 50 years. To ensure their leak tightness, concrete containment structures of NPPs shall be regularly leakage-rate tested in accordance with Integrated Leakage Rate Test (ILRT) requirements as detailed in CSA N287.7 and CNSC regulatory documents. Concrete containment structures shall be subjected to ILRTs to ensure their leak tightness, either every 6 years or conforming to a performance-based test interval agreed upon between the licensee and the Authority Having Jurisdiction (AHJ), i.e. the CNSC. To establish a performance-based ILRT interval option, in addition to inspection, testing, and aging management requirements, demonstration shall be made to the AHJ that the leakage-rate trends, maintained from commissioning to the most recent outage ILRT, are stable over at least the two most recent (post-commissioning) outage tests. Stable is defined in CSA N287.7-17 as no statistically significant increase that would cause the acceptance leakage limit to be exceeded if it changed in a linear fashion over the next test interval. The stability requirements mentioned above may be subject to interpretation. This paper discusses various options for trending leakage-rate test results and recommends a multi-step approach.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.003
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.017
GPT teacher head0.192
Teacher spread0.175 · 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 designNot applicable
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
Published2022
Admission routes1
Has abstractyes

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