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Record W4403137333 · doi:10.1088/978-0-7503-6069-2

Generation IV Nuclear Reactors

2024· book· en· W4403137333 on OpenAlexaff
R. A. Dunlap

Bibliographic record

Venuenot available
Typebook
Languageen
FieldEngineering
TopicNuclear and radioactivity studies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNuclear engineeringEngineering

Abstract

fetched live from OpenAlex

Generation IV nuclear reactors are designed to be safe, economical and to produce minimal radioactive waste material. The present book reviews the various categories of Generation IV reactors and their applications, providing an overview of the physics of nuclear power reactors and describing the technology behind each of the six Generation IV designs. It describes past progress in each of these technologies and summarizes current research and development activities. It gives an evaluation of the advantages and disadvantages of each design and summarizes the ways in which each satisfies the criteria specified by the Generation IV International Forum. This book provides an overview of the environmental aspects of nuclear power, an introduction to the physics of fission reactors and a summary of the drawbacks of current reactor designs. Key features • Provides an overview of how nuclear fission power can contribute to future low-carbon energy sources. • Discusses the environmental aspects of nuclear power, the physics of fission reactors and the drawbacks of current reactor designs. • Provides a detailed technical description of potential Generation IV fission reactors. • Analyses how each Generation IV reactor design deals with current reactor drawbacks. • Reviews previous work and current status of each Generation IV reactor design. • Considers the possibility and timescale for commercial development of each design.

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)0.000
metaresearch head score (Gemma)0.001
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: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.056
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0560.040

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 head0.021
GPT teacher head0.207
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

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
GenreOther

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

Citations7
Published2024
Admission routes1
Has abstractyes

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Same topicNuclear and radioactivity studiesFrench-language works237,207