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Comparative evaluation of different options for energy system development in small countries

2023· article· en· W4319992102 on OpenAlexaff
N Zaharieva, Pavlin Groudev, A Chaushevski, N Popov, E Kichev

Bibliographic record

VenueIOP Conference Series Earth and Environmental Science · 2023
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy Security and Policy
Canadian institutionsMcMaster University
Fundersnot available
KeywordsSustainabilityContext (archaeology)Ranking (information retrieval)Environmental economicsEnergy securitySustainable developmentBusinessRisk analysis (engineering)Computer scienceEngineeringEconomicsRenewable energyPolitical science

Abstract

fetched live from OpenAlex

Abstract In this paper an approach, proposed in the frame of the IAEA International Project on Innovative Nuclear Reactors and Fuel Cycles (INPRO) and its Collaborative Project “Key indicators for innovative nuclear energy systems” (KIND) for a multi-criteria comparative assessment of different energy options was studied and applied. Comparative evaluation of two hypothetical energy systems - nuclear and non-nuclear was performed in the context of co-operation between small countries in the Balkan region. The evaluation was based on various criteria grouped into selected areas to cover different dimensions in terms of sustainability - economic, environment, public, safety, infrastructure. The results show that nuclear option obtained higher total scores and is thus the preferred option. The nuclear option performs better in the areas of economics, environment and national security and non-nuclear option has the advantage in areas of waste management, safety, public acceptance and infrastructure. The sensitivity and uncertainty analysis performed in the study enable to consider the impact of the input uncertainties on the ranking results and their stability. The study could be used to support decision makers in energy policy at national level, demonstrating the application of methodology.

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.954
Threshold uncertainty score0.532

Codex and Gemma teacher scores by category

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

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 head0.047
GPT teacher head0.260
Teacher spread0.213 · 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 teacher head, 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

Citations0
Published2023
Admission routes1
Has abstractyes

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