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Record W1965903241 · doi:10.1115/1.4029420

Nuclear Power as a Basis for Future Electricity Generation

2014· article· en· W1965903241 on OpenAlexaff
Igor Pioro, Romney B. Duffey

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2014
Typearticle
Languageen
FieldEngineering
TopicHeat transfer and supercritical fluids
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsNuclear powerElectricity generationGeothermal energyEnergy developmentEnvironmental scienceEnergy independenceWind powerFossil fuelRenewable energyCoalEnergy sourceElectric powerElectricityThermal power stationGeothermal gradientWaste managementPower (physics)EngineeringElectrical engineeringGeologyPhysics

Abstract

fetched live from OpenAlex

It is well known that electrical power generation is the key factor for advances in industry, agriculture, technology, and standards of living. Also, a strong power industry with diverse energy sources is very important for a nation’s independence. In general, electrical energy can be generated from (1) burning mined and refined energy sources such as coal, natural gas, oil, and nuclear; and (2) harnessing energy sources such as hydro, biomass, wind, geothermal, solar, and wave power. Today, the main sources for electrical energy generation are (1) thermal power, primarily using coal and secondarily natural gas; (2) “large” hydraulic power from dams and rivers; and (3) nuclear power from various reactor designs. The balance of the energy sources is from using oil, biomass, wind, geothermal, and solar, which have a visible impact just in some countries. This paper presents the current status and role of the nuclear-power industry in the world with a comparison of nuclear-energy systems to other energy systems.

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 score0.011
Threshold uncertainty score0.037

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 studies0.0010.003
Scholarly communication0.0020.003
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.005
GPT teacher head0.209
Teacher spread0.203 · 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

Citations64
Published2014
Admission routes1
Has abstractyes

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