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Record W4312319310 · doi:10.14209/sbrt.2022.1570817447

Inteligência Artificial Aplicada a Detecção de Vazamentos em Dutos e Canos

2022· article· pt· W4312319310 on OpenAlexaff
Lucas T. da Silva, Rael da S. Oliveira, ANDERSON CORTEZ CALDERINI, José F. Rodrigues, João Dias

Bibliographic record

VenueAnais do XL Simpósio Brasileiro de Telecomunicações e Processamento de Sinais · 2022
Typearticle
Languagept
FieldDecision Sciences
TopicStock Market Forecasting Methods
Canadian institutionsCanadian Association of University Research Administrators
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsComputer science

Abstract

fetched live from OpenAlex

Resumo-Vazamentos em dutos e canos podem gerar prejuzos significativos devido perda do fluido, assim como, contaminao de solos e rios, dependendo do fluido vazado. Este trabalho, desenvolvido como projeto de iniciao cientfica, tem como objetivo criar uma ferramenta que permita aumentar a eficincia da inspeo area de dutos e canos utilizando tcnicas de aprendizado de mquina para a deteco de vazamentos. Para isso, foram treinados e testados trs modelos de redes neurais com pr-processamento de

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)0.022
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0220.022
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.008
Science and technology studies0.0040.001
Scholarly communication0.0030.001
Open science0.0100.006
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0110.000

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 head0.073
GPT teacher head0.376
Teacher spread0.303 · 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 designOther design
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
Published2022
Admission routes1
Has abstractyes

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Same venueAnais do XL Simpósio Brasileiro de Telecomunicações e Processamento de SinaisSame topicStock Market Forecasting MethodsFrench-language works237,207