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Record W4403449773 · doi:10.1080/00295450.2024.2409589

A Review of the Capabilities and Gaps in Canada’s Research Reactors for Facilitating the Development and Deployment of Small Modular Reactors

2024· review· en· W4403449773 on OpenAlexaffabout
Zoe Hoyda, Kirk D. Atkinson, Arthur Situm

Bibliographic record

VenueNuclear Technology · 2024
Typereview
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsOntario Tech UniversityUniversity of Regina
Fundersnot available
KeywordsSoftware deploymentModular designNuclear engineeringResearch reactorComputer scienceSystems engineeringBusinessEngineeringNuclear physicsOperating systemPhysicsNeutron

Abstract

fetched live from OpenAlex

With an interest in reducing carbon emissions, Canada has committed to deploying a variety of small modular reactor (SMR) designs. This review covers Canada’s research reactor capabilities relevant to SMR development and highlights gaps in these capabilities. Following the commissioning of the subcritical facility at Ontario Tech University, Canada’s capabilities in reactor physics, education, and training will be relatively sufficient. In contrast, materials/fuel irradiation capabilities and neutron scattering techniques are insufficient, with a notable gap in in-reactor test loops needed to develop new fuels. Recommendations are made for a new multipurpose research reactor (MPRR) development and deployment of SMRs in the long term while increasing accessibility to Canada’s existing research reactors in the near term, particularly in Western Canada.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.995
Threshold uncertainty score0.283

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.046
GPT teacher head0.279
Teacher spread0.234 · 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
Published2024
Admission routes2
Has abstractyes

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