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Record W2327117928 · doi:10.1109/wartia.2014.6976463

The study on spatial homogenization parameters based upon equivalence theory

2014· article· en· W2327117928 on OpenAlexaboutno aff
Jin Ma, Zhibin Liu, Bingshu Wang, Xinhui Duan

Bibliographic record

Venue2014 IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA) · 2014
Typearticle
Languageen
FieldEngineering
TopicCivil and Geotechnical Engineering Research
Canadian institutionsnot available
Fundersnot available
KeywordsHomogenization (climate)DiscretizationHomogeneousNeutron transportNuclear reactor coreNuclear reactorIsotropyDiffusion theoryEquivalence (formal languages)MathematicsComputer scienceApplied mathematicsStatistical physicsNuclear engineeringMathematical analysisPhysicsEngineeringNuclear physicsNeutron

Abstract

fetched live from OpenAlex

Homogeneous parameters of a mathematical nuclear core full power steady state were built based on the equivalence theory upon the Canada Pickering a CANDU reactor. The reactor core neutronics is based on a two energy group formulation and a typical spatial discretization employs one node per fuel assembly, each of the assemblies is further subdivided into 12 to 30 axial slices which is sufficiently detailed to account for the non-uniform aspect of reactor core composition. This provides us a good reference solution from which all the nodal nuclear parameters can be calculated. The detailed homogeneous parameters are got which can be a reference as the nodal diffusion methods to resolve the homogeneous parameters problem.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
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 score0.571
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.028
GPT teacher head0.323
Teacher spread0.296 · 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.

Study designSimulation or modeling
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
Published2014
Admission routes1
Has abstractyes

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