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Record W4390095678 · doi:10.59254/sbpo-2018-85431

AVALIAÇÃO DOS IMPACTOS DA REPRESENTAÇÃO DA GERAÇÃO EÓLICA NA CONFIABILIDADE MULTI-ÁREA

2018· article· pt· W4390095678 on OpenAlexaff
T.C. Justino, Miryam Gerk Curt, Carmen L.T. Borges, José Francisco Moreira Pessanha, Luiz Guilherme Barbosa Marzano

Bibliographic record

VenueAnais do Simpósio Brasileiro de Pesquisa Operacional · 2018
Typearticle
Languagept
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsIntertek (Canada)
Fundersnot available
KeywordsReliability (semiconductor)PhysicsThermodynamicsPower (physics)

Abstract

fetched live from OpenAlex

RESUMOO setor elétrico brasileiro vem passando por mudanças no perfil do seu parque gerador, com aumento da participação de fontes renováveis intermitentes, especialmente a geração eólica.É importante que esta fonte seja representada adequadamente nos estudos de planejamento da expansão e da operação.Assim, este artigo propõe um aprimoramento na representação da geração eólica nos estudos de confiabilidade multi-área, de modo a considerar a incerteza e a intermitência dessa fonte.A representação proposta da geração eólica foi aplicada a um estudo de caso para uma configuração do sistema interligado nacional.Resultados numéricos são apresentados e discutidos.

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)0.005
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.321
Teacher spread0.274 · 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 designSimulation or modeling
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
Published2018
Admission routes1
Has abstractyes

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