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Record W2077892033 · doi:10.1093/ijlct/2.2.109

Nuclear energy as a component of sustainable energy systems

2007· article· en· W2077892033 on OpenAlexaffabout
Marc A. Rosen, İbrahim Dinçer

Bibliographic record

VenueInternational Journal of Low-Carbon Technologies · 2007
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsCogenerationFossil fuelSustainable developmentComponent (thermodynamics)Sustainable energyEnvironmental economicsEnergy planningEnergy (signal processing)Energy systemBusinessNatural resource economicsSustainabilityEfficient energy useEnvironmental scienceWaste managementRenewable energyEngineeringElectricity generationEconomicsPower (physics)Ecology

Abstract

fetched live from OpenAlex

Abstract Achieving sustainable solutions to today's energy, environmental, and sustainable development problems requires long-term planning and actions. Energy issues are particularly prevalent at present and nuclear energy, despite the ongoing debate, appears to provide one component of an effective sustainable system. In this paper we investigate increasing the utilization efficiency of energy resources and reducing environmental emissions to achieve more sustainable development, focusing on utility-scale cogeneration and contributions of nuclear energy. A case study is presented for Ontario using the nuclear and fossil facilities of the main provincial electrical utilities. It is observed that implementation of utility-based cogeneration in Ontario can contribute to a sustainable future by reducing significantly annual and cumulative uranium and fossil fuel use and related emissions, providing economic benefits for the province and its electrical utilities, and allowing nuclear energy to be substituted for fossil fuels.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.258
Teacher spread0.250 · 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 designTheoretical or conceptual
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

Citations10
Published2007
Admission routes2
Has abstractyes

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