MétaCan
Menu
Back to cohort
Record W3014553315 · doi:10.3808/jeil.202000026

Perspective on Site Selection of Small Modular Reactors

2020· article· en· W3014553315 on OpenAlexaff
XinLi Zhang, Guohe Huang, Lirong Liu, J. P. Chen, Bo Luo, Yen‐Pei Fu, Xiyue Zheng, David Han, Yanyan Liu

Bibliographic record

VenueJournal of Environmental Informatics Letters · 2020
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsSite selectionSelection (genetic algorithm)Modular designNuclear powerRisk analysis (engineering)Software deploymentProcess (computing)Computer scienceEngineeringBusinessPolitical science

Abstract

fetched live from OpenAlex

As an emerging means of energy supply, small modular reactors (SMRs) are considered as a promising option for relieving environmental pressure caused by increasing fossil fuel consumption. Since SMRs are at an early stage of development, in-depth analysis on the necessity and feasibility of their deployment is essential. The site selection of SMRs, which is a multifaceted process and should be guided by a clearly established set of criteria, is a crucial step. To propose comprehensive recommendations for SMR site selection, a review is provided in this study. The review involves development process, technical characteristics and potential applications of SMRs, research status of SMR site selection, and criteria for site selection of nuclear power plants (NPPs). Different considerations of siting criteria between SMRs and NPPs are analyzed. Based on the review and analyses, perspectives and targeted suggestions on SMR site selection are provided.

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)0.003
metaresearch head score (Gemma)0.003
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: Commentary · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.203
Teacher spread0.191 · 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
GenreCommentary

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

Citations14
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Environmental Informatics LettersSame topicGraphite, nuclear technology, radiation studiesFrench-language works237,207