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Record W2580660336 · doi:10.1115/1.4035726

Benchmarking Severe Accident Computer Codes for Heavy Water Reactor Applications

2017· article· en· W2580660336 on OpenAlexaff
Thambiayah Nitheanandan, Xuewu Cao, Jongho Choi, Daniel Dupleac, D.-H. Kim, H.G. Lele, Arun K. Nayak, H. P. Rammohan

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2017
Typearticle
Languageen
FieldEngineering
TopicNuclear Engineering Thermal-Hydraulics
Canadian institutionsCanadian Nuclear Laboratories
Fundersnot available
KeywordsBenchmarkingComputer scienceCode (set theory)Accident (philosophy)Nuclear engineeringSystems engineeringEngineeringBusinessSet (abstract data type)Programming language

Abstract

fetched live from OpenAlex

The International Atomic Energy Agency (IAEA) organized a coordinated research project (CRP) on “Benchmarking Severe Accident Computer Codes for Heavy Water Reactors (HWR) Applications,” (IAEA TECDOC Series No. 1727), and the activity was completed in 2012. This paper summarizes the results from the CRP: the selection of a severe accident sequence, definition of appropriate geometrical and boundary conditions, benchmarking code analyses, comparison of the code results, evaluation of the capabilities of existing computer codes to predict important severe accident phenomena, and suggestions for code improvements and/or new experiments to reduce uncertainties.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.008
GPT teacher head0.228
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations27
Published2017
Admission routes1
Has abstractyes

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