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Record W3202692391 · doi:10.3968/12236

Research on Safety Evaluation of Nuclear Power Plant Based on Entropy Weight Method

2021· article· en· W3202692391 on OpenAlexvenueno aff
Shijie Zhou

Bibliographic record

VenueCanadian social science · 2021
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear power plantNuclear powerChinaRisk assessmentIndex (typography)Nuclear engineeringComputer scienceEnvironmental scienceRisk analysis (engineering)Computer securityBusinessPolitical scienceEngineeringPhysicsLawNuclear physics

Abstract

fetched live from OpenAlex

Since the 18th National Congress of the Communist Party of China, China’s nuclear security has entered a new era of safety and efficiency. At the same time, the first white paper “China’s nuclear security” emphasizes the need to deal with various nuclear security challenges and maintain nuclear security. In this paper, an index system of nuclear power plant safety assessment is constructed, which includes three first-class indexes: internal risk assessment, external risk assessment and human risk assessment of nuclear power plant. Each index is weighted and evaluated by entropy weight method, and the safety of all nuclear power plants in operation during 2013-2018 in China is researched vertically as well as Ling’ao, Yangjiang, Ningde and Fangchenggang nuclear power plant is researched horizontally.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.184
GPT teacher head0.479
Teacher spread0.295 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

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