MétaCan
Menu
Back to cohort
Record W2766796381 · doi:10.1115/pvp2017-65821

Considerations for the Use of Probabilistic Assessments in Regulatory Decision Making Related to Pressure Boundary Component Aging

2017· article· en· W2766796381 on OpenAlexaffabout
Carroll Blair, John C. Jin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsCanadian Nuclear Safety Commission
Fundersnot available
KeywordsProbabilistic logicRisk analysis (engineering)ConservatismComponent (thermodynamics)Computer scienceCommissionService (business)Reliability engineeringNuclear power plantRisk managementBoundary (topology)Risk assessmentResource (disambiguation)Nuclear powerOperations researchActuarial scienceBusinessEngineeringComputer securityArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Within the current Canadian regulatory framework, the structural integrity of pressure boundary components with detected service-induced degradation must be demonstrated using deterministic evaluation techniques. However, Canadian Nuclear Safety Commission staff has recognized that the inherent conservatism in these deterministic assessment approaches may generate overly conservative conclusions when they are applied to assess the impact of postulated service-induced degradation to establish aging management requirements for nuclear power plant pressure boundary components. This may have the unintended effect of reducing the effectiveness of aging management programs by directing resources towards activities that will have minimal benefit on improving plant safety and could result in unnecessary dose to personnel. With this in mind, CNSC staff has accepted the limited use of probabilistic assessments prepared by licensees to support aging management activities for pressure boundary components. These probabilistic assessments form a part of risk-informed decision making strategies intended to reduce excess conservatism that could arise if decisions are based solely on the results of deterministic assessments. This paper provides an overview of CNSC staff’s experiences with the review and acceptance of licensee submissions incorporating probabilistic assessments of pressure boundary component aging for risk informed decision making.

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)0.003
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.221
GPT teacher head0.450
Teacher spread0.229 · 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.

Study designSimulation or modeling
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
Published2017
Admission routes2
Has abstractyes

Explore more

Same topicRisk and Safety AnalysisFrench-language works237,207