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Record W2099590124 · doi:10.1093/rpd/ncu301

Characterisation of the TRIUMF neutron facility using a Monte Carlo simulation code

2014· article· en· W2099590124 on OpenAlexfundaboutno aff
Stephen Monk, T. Abram, Malcolm J. Joyce

Bibliographic record

VenueRadiation Protection Dosimetry · 2014
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsnot available
FundersTRIUMFNuclear Decommissioning Authority
KeywordsPhysicsNeutronMonte Carlo methodNuclear physicsNeutron fluxCosmic rayNuclear engineering

Abstract

fetched live from OpenAlex

Here, the characterisation of the high-energy neutron field at TRIUMF (The Tri Universities Meson Facility, Vancouver, British Columbia) with Monte Carlo simulation software is described. The package used is MCNPX version 2.6.0, with the neutron fluence rate determined at three locations within the TRIUMF Thermal Neutron Facility (TNF), including the exit of the neutron channel where users of the facility can test devices that may be susceptible to the effects of this form of radiation. The facility is often used to roughly emulate the field likely to be encountered at high altitudes due to radiation of galactic origin and thus the simulated information is compared with the energy spectrum calculated to be due to neutron radiation of cosmic origin at typical aircraft altitudes. The calculated values were also compared with neutron flux measurements that were estimated using the activation of various foils by the staff of the facility, showing agreement within an order of magnitude.

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)0.002
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.015
GPT teacher head0.208
Teacher spread0.193 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

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