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Record W2473356672 · doi:10.1093/rpd/ncv402

NEUTRON GENERATOR FACILITY AT SFU: GEANT4 DOSE RATE PREDICTION AND VERIFICATION

2015· article· en· W2473356672 on OpenAlexafffund
J. Williams, A. Chester, T. Domingo, U. Rizwan, K. Starosta, P. Voss

Bibliographic record

VenueRadiation Protection Dosimetry · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsSimon Fraser University
FundersCanadian Nuclear Safety CommissionCERN
KeywordsNeutron generatorNuclear engineeringNeutronMonte Carlo methodNuclear physicsPhysicsGenerator (circuit theory)RadiationNeutron temperatureEnvironmental scienceEquivalent doseMathematicsEngineeringStatisticsPower (physics)

Abstract

fetched live from OpenAlex

Detailed dose rate maps for a neutron generator facility at Simon Fraser University were produced via the GEANT4 Monte Carlo framework. Predicted neutron dose rates throughout the facility were compared with radiation survey measurements made during the facility commissioning process. When accounting for thermal neutrons, the prediction and measurement agree within a factor of 2 or better in most survey locations, and within 10 % inside the vault housing the neutron generator.

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)0.003
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: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.003

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 head0.021
GPT teacher head0.234
Teacher spread0.213 · 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

Citations4
Published2015
Admission routes2
Has abstractyes

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