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Record W2801089249 · doi:10.1002/9781119494096.ch26

Nuclear Fuel Modelling and Perspectives on Canadian Efforts in Fuel Development

2018· other· en· W2801089249 on OpenAlexafffundabout
M.H.A. Piro, Andrew A. Prudil, M. J. Welland, W.R. Richmond, A. Bergeron, E. Torres, Christopher I. Maxwell, Jeremy Pencer, Nigel Harrison, M. Floyd

Bibliographic record

VenueCeramic transactions /Ceramic transactions · 2018
Typeother
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCanadian Nuclear LaboratoriesOntario Tech University
FundersCanada Research Chairs
KeywordsNuclear fuelNuclear engineeringCeramicScale (ratio)Nuclear reactorUraniumStructural materialMaterials scienceEngineeringPhysicsMetallurgy

Abstract

fetched live from OpenAlex

This chapter discusses how computational efforts span the entire multi-scale multi-physics spectrum, ranging from atomistic electronic structure and classical inter-atomic potential calculations, to meso-scale simulations of micro-structural evolution, to continuum scale thermo-mechanical simulations of fuel performance. A variety of scientific disciplines must be understood in regards to nuclear fuel behavior, including heat transfer, structural and fracture mechanics, thermodynamics, materials science, and reactor physics. A great difficulty in capturing the interdependencies of the foregoing mechanisms is that it is impossible to reproduce the conditions inside of a nuclear reactor in the laboratory. Canadian interests in nuclear fuel research and development have been mainly focused on uranium dioxide ceramic fuels, which are used in CANDU nuclear generating stations, and to a lesser extent in metallic dispersion fuels, which are used in research and test reactors.

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 categoriesMeta-epidemiology (narrow), Insufficient 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: none
Teacher disagreement score0.922
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0270.001

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 head0.022
GPT teacher head0.211
Teacher spread0.189 · 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

Citations2
Published2018
Admission routes3
Has abstractyes

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