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Record W2396417417 · doi:10.12943/anr.2014.00037

AN MCNP PARAMETRIC STUDY OF GEORGE C. LAURENCE'S SUBCRITICAL PILE EXPERIMENT

2014· article· en· W2396417417 on OpenAlexafffundvenueabout
Ruxandra Dranga, L. Blomeley, Rebecca Carrington

Bibliographic record

VenueAECL Nuclear Review · 2014
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsMcGill UniversityAtomic Energy (Canada)
FundersCanadian Nuclear Safety CommissionAtomic Energy of Canada Limited
KeywordsGeorge (robot)Nuclear engineeringNuclear physicsCriticalityNuclear scienceNuclear fissionNuclear dataFissionParametric statisticsPhysicsEngineeringNeutronArt historyHistoryMathematics

Abstract

fetched live from OpenAlex

In the early 1940s at the National Research Council (NRC) Laboratories in Ottawa, Canada, Dr. George Laurence conducted several experiments to determine if a sustained nuclear fission chain reaction in a carbon–uranium arrangement (or “pile”) was possible. Although Dr. Laurence did not achieve criticality, these pioneering experiments marked a significant historical event in nuclear science, and they provided a valuable reference for subsequent experiments that led to the design of Canada’s first heavy-water reactors at the Chalk River Nuclear Laboratories. This paper summarizes the results of a recent collaborative project between Atomic Energy of Canada Limited and the Deep River Science Academy undertaken to numerically explore the experiments carried out at the NRC Laboratories by Dr. Laurence, while teaching high school students about nuclear science and technology. In this study, a modern Monte Carlo reactor physics code, MCNP6, was utilized to identify and study the key parameters impacting the subcritical pile’s neutron multiplication factor (e.g., moderation, geometry, material impurities) and quantify their effect on the extent of subcriticality. The findings presented constitute the first endeavour to model, using a current computational reactor physics tool, the seminal experiment that provided the foundation of Canada’s nuclear science and technology program.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.241
Teacher spread0.231 · 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.

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

Citations0
Published2014
Admission routes4
Has abstractyes

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