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Record W1582052495

THE STATUS AND PROSPECT OF DUPIC FUEL TECHNOLOGY

2006· article· en· W1582052495 on OpenAlexaboutno aff
Myung Seung Yang, Hangbok Choi, Chang Joon Jeong, Kee Chan Song, Jung Won Lee, Geun Il Park, Ho Dong Kim, Won Il Ko, Jang Jin Park, Ki‐Ho Kim, Ho Hee Lee, Joo Hwan Park

Bibliographic record

VenueNuclear Engineering and Technology · 2006
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsNuclear engineeringFuel cycleSpent nuclear fuelSpent fuel poolWaste managementFuel element failureDepleted uraniumEngineeringNuclear fuelUraniumEnvironmental scienceNuclear physicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

Since 1991, Korea, Canada and United States have performed the direct use of spent pressurized water reactor (PWR) fuel in the Canada deuterium uranium (CANDU) reactors (DUPIC) fuel development project. Unlike the Tandem fuel cycle, which requires a wet reprocessing, the DUPIC fuel technology can directly refabricate CANDU fuels from the PWR spent fuel and, therefore, is recognized as a highly proliferation-resistant fuel cycle technology, which can be adopted even in non-proliferation treaty countries. The Korea Atomic Energy Research Institute (KAERI) has fabricated DUPIC fuel elements in a laboratory- scale remote fuel fabrication facility. KAERI has demonstrated the fuel performance in the research reactor, and has confirmed the operational feasibility and safety of a CANDU reactor loaded with the DUPIC fuel using conventional design and analysis tools, which will be the foundation of the future practical and commercial uses of DUPIC fuel.

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)0.001
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.003
GPT teacher head0.158
Teacher spread0.156 · 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

Citations42
Published2006
Admission routes1
Has abstractyes

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