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Record W3045406451 · doi:10.2298/ntrp2001024g

Verification and validation of SuperMC3.2 with heavy water reactor model DCA

2020· article· en· W3045406451 on OpenAlexaboutno aff
Quan Gan, Shengpeng Yu, Lijuan Hao, Jing Song, L LongPengcheng

Bibliographic record

VenueNuclear Technology and Radiation Protection · 2020
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
Fundersnot available
KeywordsHeavy waterNuclear engineeringDeuteriumUniversality (dynamical systems)NeutronNuclear dataNuclear physicsComputer scienceEnvironmental sciencePhysicsEngineering

Abstract

fetched live from OpenAlex

The super multi-functional calculation program for nuclear design and safety evaluation is a general, intelligent, accurate and precise simulation software system for the nuclear design and safety evaluations. The heavy water reactor has a much stronger moderation power and much longer diffusion length of the thermalized neutrons. The paper intends to show the verification and validation of SuperMC3.2 with a heavy-water-moderated lattice named the deuterium critical assembly which is very similar to the Canada Deuterium Uranium type reactor and selected from the international reactor physics experiment evaluation project. The calculation results were compared with the reference calculated results and the experimental data from International Reactor Physics Experiment Evaluation Project. The final obtained results proved the accuracy, convenience and universality of SuperMC, and primarily verified the applicability of SuperMC in nuclear analysis of heavy water reactor.

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)0.001
metaresearch head score (Gemma)0.003
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: none
Teacher disagreement score0.025
Threshold uncertainty score0.051

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.162
Teacher spread0.154 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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