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Record W2135372734

Estimate of the Sources of Plutonium-Containing Wastes Generated from MOX Fuel Production in Russia

2002· article· en· W2135372734 on OpenAlexaboutno aff
K. G. Kudinov, А. А. Тretyakov, Yu. P. Sorokin, V. V. Bondin, L. F. Manakova, L.J. Jardine

Bibliographic record

VenueUniversity of North Texas Digital Library (University of North Texas) · 2002
Typearticle
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsnot available
FundersLawrence Livermore National LaboratoryU.S. Department of Energy
KeywordsMOX fuelPlutoniumVVERWaste managementEnvironmental scienceSpent nuclear fuelPlutonium-240EngineeringNuclear engineeringRadiochemistryPlutonium-239ChemistryNuclear physics
DOInot available

Abstract

fetched live from OpenAlex

In Russia, mixed oxide (MOX) fuel is produced in a pilot facility “Paket ” at “MAYAK” Production Association. The Mining-Chemical Combine (MCC) has developed plans to design and build a dedicated industrial-scale plant to produce MOX fuel and fuel assemblies (FA) for VVER-1000 water reactors and the BN-600 fast-breeder reactor, which is pending an official Russian Federation (RF) site-selection decision. The design output of the plant is based on a production capacity of 2.75 tons of weapons plutonium per year to produce the resulting fuel assemblies: 1.25 tons for the BN-600 reactor FAs and the remaining 1.5 tons for VVER-1000 FAs. It is likely the quantity of BN-600 FAs will be reduced in actual practice. The process of nuclear disarmament frees a significant amount of weapons plutonium for other uses, which, if unutilized, represents a constant general threat. In France, Great Britain, Belgium, Russia, and Japan, reactor-grade plutonium is used in MOX-fuel production. Making MOX-fuel for CАNDU (Canada) and pressurized water reactors (PWR) (Europe) is under consideration in Russia. If this latter production is added, as many as 5 tons of Pu per year might be processed into new FAs in Russia.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.009
GPT teacher head0.149
Teacher spread0.139 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2002
Admission routes1
Has abstractyes

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Same venueUniversity of North Texas Digital Library (University of North Texas)Same topicNuclear and radioactivity studiesFrench-language works237,207