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Record W3110195581 · doi:10.29333/ejosdr/9305

Nuclear Energy: Non-Electric Applications

2020· article· en· W3110195581 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEuropean Journal of Sustainable Development Research · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsUniversity of Ontario Institute of Technology
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNuclear powerCogenerationElectricityElectric powerElectricity generationEnvironmental scienceEnergy securityElectric potential energyEnergy (signal processing)Renewable energyEnvironmental economicsEngineeringPower (physics)Electrical engineeringPhysicsEconomicsNuclear physics

Abstract

fetched live from OpenAlex

Nuclear energy is often viewed as linked exclusively to electrical power generation. However, the applications for nuclear energy are significantly greater than only electricity, and include cogeneration, district heating and cooling, high-temperature process heating, hydrogen and alternative fuel production, transportation and desalination. These additional applications expand the prospects for nuclear energy notably, and enhance the benefits that can be derived from it, such as reduced environmental impact and climate change mitigation. Interest in non-electric applications of nuclear energy is growing for environmental, economic, security and other reasons. In this paper, non-electric applications of nuclear energy are reviewed, including technological, environmental and economic issues of such applications as well as future prospects and benefits of non-electric applications of nuclear energy.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.027
GPT teacher head0.249
Teacher spread0.223 · 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