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Record W3138377232 · doi:10.1115/1.4050478

Small Modular Reactors: Learning From the Past

2021· article· en· W3138377232 on OpenAlexaffabout
Esam M.A. Hussein

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2021
Typearticle
Languageen
FieldEngineering
TopicNuclear reactor physics and engineering
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsModular designNuclear engineeringLiquid metalNuclear reactorMolten saltCoolantEnvironmental scienceWater cooledMaterials scienceProcess engineeringComputer scienceWater coolingMechanical engineeringEngineeringMetallurgy

Abstract

fetched live from OpenAlex

Abstract Most emerging small modular reactor (SMR) designs resemble older reactors that were designed in the early days of the nuclear technology. Experience with operating these old reactors can contribute to the licensing of new SMRs, by showing that some safety concepts were already proven, and their viability was demonstrated. This paper shows examples of older reactors in each of the reemerging SMR concepts of integrated pressurized water reactors, high temperature gas cooled reactors, molten salt reactors, and liquid metal cooled fast reactors. The Canadian experience with the WR-1 organic cooled reactor is also discussed to examine whether it can inform the development on an organic cooled SMR.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.373
Threshold uncertainty score0.293

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 science0.0000.000
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 head0.010
GPT teacher head0.177
Teacher spread0.167 · 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 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
Published2021
Admission routes2
Has abstractyes

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