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Record W2023767055 · doi:10.1115/icone21-16913

CNSC Review of Safety Improvements: CANDU Fuel Modification in Canada

2013· article· en· W2023767055 on OpenAlexaffabout
Yujun Guo

Bibliographic record

VenueVolume 1: Plant Operations, Maintenance, Engineering, Modifications, Life Cycle and Balance of Plant; Nuclear Fuel and Materials; Radiation Protection and Nuclear Technology Applications · 2013
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCanadian Nuclear Safety Commission
Fundersnot available
KeywordsNuclear powerEnvironmental scienceOffset (computer science)Nuclear fuelNuclear engineeringMargin (machine learning)EngineeringComputer scienceNuclear physics

Abstract

fetched live from OpenAlex

Aging of a CANDU nuclear power plant affects various safety margins of the plant. Margin to fuel sheath dryout is one of the safety margins that have been detrimentally affected, leading to a reduced margin to dryout with time. If no proactive actions are taken, the plant will have to de-rate its operation at an earlier time. To postpone the de-rating, the Canadian nuclear Industry has taken multi-initiatives to restore, or partially restore the safety margins that have been eroded due to plant aging. One of the initiatives is modification/re-optimization of the current fuel design, in order to improve the fuel thermalhydraulic performance, i.e., to suppress fuel sheath dryout, whereby offset partially the erosion of margin to fuel sheath dryout. In response to the initiative of fuel bundle modification, the Canadian Nuclear Safety Commission (CNSC) — the nuclear safety regulator — has set clear requirements and expectations and followed rigours processes and procedures for reviewing and licensing the modified fuel. This paper summarizes the fuel modification program in Canada, and the CNSC requirements, expectations, and review processes associated with licensing review of fuel modifications in Canada.

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 score0.889
Threshold uncertainty score0.961

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 head0.005
GPT teacher head0.170
Teacher spread0.165 · 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

Citations1
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueVolume 1: Plant Operations, Maintenance, Engineering, Modifications, Life Cycle and Balance of Plant; Nuclear Fuel and Materials; Radiation Protection and Nuclear Technology ApplicationsSame topicNuclear Materials and PropertiesFrench-language works237,207