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Record W3154297230 · doi:10.3233/jnr-210012

A prototype compact accelerator-based neutron source (CANS) for Canada

2021· preprint· en· W3154297230 on OpenAlexafffundabout
Robert Laxdal, Dalini Maharaj, Mina Abbaslou, Z. Tun, Daniel Banks, A. Gottberg, M. Marchetto, Eduardo Rodríguez, Z. Yamani, H. Fritzsche, R. B. Rogge, Ming Pan, O. Kester, Drew Marquardt

Bibliographic record

VenueJournal of Neutron Research · 2021
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsCanadian Nuclear LaboratoriesWindsor Regional HospitalUniversity of VictoriaWestern UniversityUniversity of WindsorTRIUMF
FundersUniversity of Windsor
KeywordsNeutron sourceNeutronSpallationNeutron captureNuclear physicsNuclear engineeringNeutron scatteringNeutron fluxPhysicsLinear particle acceleratorEnvironmental scienceComputer scienceEngineeringBeam (structure)Optics

Abstract

fetched live from OpenAlex

Canada’s access to neutron beams for neutron scattering was significantly curtailed in 2018 with the closure of the National Research Universal (NRU) reactor in Chalk River, Ontario, Canada. New sources are needed for the long-term; otherwise, access will only become harder as the global supply shrinks. Compact Accelerator-based Neutron Sources (CANS) offer the possibility of an intense source of neutrons with a capital cost significantly lower than spallation sources. In this paper, we propose a CANS for Canada. The proposal is staged with the first stage offering a medium neutron flux, linear accelerator-based approach for neutron scattering that is also coupled with a boron neutron capture therapy (BNCT) station and a positron emission tomography (PET) isotope production station. The first stage will serve as a prototype for a second stage: a higher brightness, higher cost facility that could be viewed as a national centre for neutron applications.

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)0.001
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.374
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.078
GPT teacher head0.378
Teacher spread0.300 · 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
Published2021
Admission routes3
Has abstractyes

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