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Record W2552356091

The Current Status on DUPIC Fuel Technology Development

2006· article· en· W2552356091 on OpenAlexaboutno aff
KC Song, Hangbok Choi, Heuy Dong Kim, JJ Park, GI Park, KH Kang, Jin‐Won Lee, Yang

Bibliographic record

VenuePacific Basin Nuclear Conference 2006 · 2006
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsnot available
Fundersnot available
KeywordsNatural uraniumSpent nuclear fuelBurnupSpent fuel poolFissile materialFuel element failureWaste managementNuclear engineeringThorium fuel cycleUraniumMOX fuelNuclear fuelEnvironmental scienceDepleted uraniumEnriched uraniumNuclear reactor coreEngineeringMaterials scienceNuclear physicsMetallurgyNeutronPhysics
DOInot available

Abstract

fetched live from OpenAlex

The Direct Use of Spent Pressurized Water Reactor (PWR) Fuel in Canada Deuterium Uranium (CANDU) Reactors (DUPIC) fuel technology has been developed by Korea, Canada and the United States (U.S.) since 1991 in order to utilize the PWR spent fuel in the CANDU reactor. The optimum fuel fabrication process was determined as the Oxidation and Reduction of Oxide Fuel (OREOX), based on the results of a feasibility study performed until 1993. Because the OREOX process uses only the thermal/mechanical process, the spent fuel standards are maintained throughout the process and the process is recognized as the most proliferation-resistant technology. In addition, because the amount of residual fissile isotopes in the PWR spent fuel is twice that of the natural uranium, the fuel burnup of the DUPIC fuel is twice that of the natural uranium fuel in the CANDU reactor. Therefore, as shown a direct disposal of the PWR spent fuel is no longer necessary, the natural uranium resources are preserved, and the amount of spent fuel from the CANDU reactor can be halved in the DUPIC fuel cycle. This paper summarizes the technical feasibility of the DUPIC fuel based on the research results obtained until now.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.889
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.005

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 head0.015
GPT teacher head0.215
Teacher spread0.200 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2006
Admission routes1
Has abstractyes

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