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Record W2413825089 · doi:10.15675/gepros.v11i2.1419

Metodologia híbrida Wavelet – SVR para projeção de deslocamentos relativos no bloco I11 da barragem da usina hidrelétrica de Itaipu

2016· article· pt· W2413825089 on OpenAlexaff
Tásia Hickmann, Liliana Madalena Gramani, Eloy Kaviski, Luíz Albino Teixeira Júnior, Samuel Bellido Rodrigues

Bibliographic record

VenueGEPROS. Gestão da Produção, Operações e Sistemas · 2016
Typearticle
Languagept
FieldEngineering
TopicDam Engineering and Safety
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsWaveletHydroelectricityMathematicsStatisticsGeologyComputer scienceEconometricsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Este artigo propõe uma metodologia híbrida para a previsão das séries temporais de deslocamentos relativos em um bloco da barragem da usina hidrelétrica de Itaipu, que integra os seguintes métodos numéricos: decomposição wavelet, support vector Machine e combinação linear de previsões. Todos os resultados estatísticos alcançados pela metodologia proposta foram mais acurados do que outras técnicas tradicionais (usadas aqui como benchmark), encorajando a sua adoção para tal finalidade.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.002

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 head0.043
GPT teacher head0.262
Teacher spread0.220 · 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; both teacher heads agree on what is shown here.

Study designBench or experimental
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
Published2016
Admission routes1
Has abstractyes

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