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Record W2557428972 · doi:10.1016/j.net.2016.11.005

A Study on the Application of CRUDTRAN Code in Primary Systems of Domestic Pressurized Heavy-Water Reactors for Prediction of Radiation Source Term

2016· article· en· W2557428972 on OpenAlexaboutno aff
Jong Soon Song, Hoon Jo Cho, Min Young Jung, Sang Heon Lee

Bibliographic record

VenueNuclear Engineering and Technology · 2016
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsnot available
FundersNational Research Foundation of KoreaKorea Atomic Energy Research InstituteMinistry of Science, ICT and Future PlanningNational Research Foundation
KeywordsNuclear decommissioningNuclear engineeringNuclear power plantEnvironmental scienceNuclear powerPressurized water reactorShutdownWaste managementTerm (time)RadiationEngineeringNuclear physics

Abstract

fetched live from OpenAlex

The importance of developing a source-term assessment technology has been emphasized owing to the decommissioning of Kori nuclear power plant (NPP) Unit 1 and the increase of deteriorated NPPs. We analyzed the behavioral mechanism of corrosion products in the primary system of a pressurized heavy-water reactor-type NPP. In addition, to check the possibility of applying the CRUDTRAN code to a Canadian Deuterium Uranium Reactor (CANDU)-type NPP, the type was assessed using collected domestic onsite data. With the assessment results, it was possible to predict trends according to operating cycles. Values estimated using the code were similar to the measured values. The results of this study are expected to be used to manage the radiation exposures of operators in high-radiation areas and to predict decommissioning processes in the primary system.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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

Citations20
Published2016
Admission routes1
Has abstractyes

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