MétaCan
Menu
Back to cohort

Overview of Recent Research Activities of Monte Carlo Simulation in Japan

2000· article· en· W2053868262 on OpenAlexaff
Kiyoshi Sakurap, Toshihiro Yamamoto, Kohtaro Ueki, Yasushi Nomura, Yoshitaka Naito

Bibliographic record

VenueJournal of Nuclear Science and Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsMonte Carlo methodMonte Carlo molecular modelingDynamic Monte Carlo methodQuantum Monte CarloStatistical physicsComputer sciencePhysicsMarkov chain Monte CarloMathematicsStatistics

Abstract

fetched live from OpenAlex

This paper describes recent progresses of the Monte Carlo simulation technology in nuclear energy field in Japan. Radiation shielding solution method using the Monte Carlo had been validated as a reliable tool through the discussion of “Radiation Shielding Safety Demonstration Analysis Group” of Japan Atomic Energy Research Institute. Since 1996, “Monte Carlo Simulation Working Group” has been accumulating use experiences of Monte Carlo codes in the wide range of nuclear energy field. This working group is planing to publish “Guideline of Monte Carlo Simulations” during FY-99. This “Guideline” is expected to be a first Japanese practical textbook of Monte Carlo calculation. In 1998, the first full-scale topical conference on Monte Carlo simulation was held in Tokyo. “Research Committee on Particle Simulation with the Monte Carlo Method” was established in Atomic Energy Society of Japan in 1998. This committee is composed of more than seventy members from many fields of nuclear energy research in Japan. This committee is expected to be a core that will drive the research and development activity of Monte Carlo calculation in Japan.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.007
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.002

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 head0.051
GPT teacher head0.316
Teacher spread0.264 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Nuclear Science and TechnologySame topicNuclear reactor physics and engineeringFrench-language works237,207