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Record W4206585612 · doi:10.1002/wene.429

A hopeless pursuit? National efforts to promote small modular nuclear reactors and revive nuclear power

2022· article· en· W4206585612 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueWiley Interdisciplinary Reviews Energy and Environment · 2022
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGovernment (linguistics)Nuclear powerModular designScale (ratio)BusinessPolitical scienceEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract Nuclear power plant construction has historically been challenged by problems of high cost, cost escalation, and construction delays. The newest set of large reactor projects have also been overbudget and overtime. This has prompted interest in new reactor technologies that proponents claim would not suffer these problems, specifically small modular reactors (SMRs), a class that encompasses a wide range of technologies. This article examines national efforts in three countries, Canada, the UK, and the United States, which are pursuing SMRs vigorously and where the government has funded their development generously. We compare the different strategies and foci of these national strategies, analyzing the various forms of support offered by the separate agencies of the government, and the private companies that are trying to develop SMRs. We also offer an overview of the different types of reactor technologies being pursued in these different countries. Following these, we outline the main challenge confronting SMR technologies: their ability to generate electricity in an economically competitive manner, highlighting the problems resulting from economies of scale being lost. By examining the experience so far, we find that even designs based on well‐tested technology cannot be deployed till after 2030 and the more radical designs might never be. This article is categorized under: Policy and Economics > Research and Development Policy and Economics > Regional and International Strategies Energy and Power Systems > Energy Infrastructure

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score1.000

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 science0.0000.001
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 head0.009
GPT teacher head0.195
Teacher spread0.186 · 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