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Record W106235518 · doi:10.13182/nse02-a2310

A Spent-Fuel Combination Method for Minimizing Composition Variability of DUPIC Fuel

2002· article· en· W106235518 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueNuclear Science and Engineering · 2002
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
Fundersnot available
KeywordsFissile materialSpent nuclear fuelNuclear engineeringPressurized water reactorMOX fuelFuel element failureEnvironmental scienceSpent fuel poolThorium fuel cycleWaste managementRadiochemistryPlutoniumChemistryNeutronNuclear physicsEngineeringPhysics

Abstract

fetched live from OpenAlex

A combination method of spent pressurized water reactor (PWR) fuel is proposed that adjusts the fuel composition for direct use of spent PWR fuel in Canada deuterium uranium (CANDU) reactors (DUPIC). This method reduces the composition heterogeneity (variation) of the DUPIC fuel caused by directly reusing spent PWR fuel as DUPIC fuel feedstock. In this study, a combination method was used to find the optimum mixture composition from the spent PWR fuel database by minimizing the composition variation of the major fissile isotopes 235U and 239Pu. The simulation results have shown that the combination method can reduce the composition variation of 235U and 239Pu to 0.11 and 1.40%, respectively, through assemblywise mixing operation only. It is also believed that the result could be improved further through a rodwise combination technique if the isotopic composition of each spent PWR fuel rod is known by direct measurement during the DUPIC fuel fabrication process.

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.

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 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 score0.937
Threshold uncertainty score0.571

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.013
GPT teacher head0.212
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