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Record W3043845563 · doi:10.15202/25254146.2018v3n1p1

DESENVOLVIMENTO DE ROTINAS AUTOMATIZADAS PARA O DIMENSIONAMENTO DE PERFIS FORMADOS A FRIO SUBMETIDOS A SOLICITAÇÕES COMBINADAS

2020· article· pt· W3043845563 on OpenAlexaff
Felipe Castelli Sasso, Fernando Busato Ramires

Bibliographic record

VenueProjectus · 2020
Typearticle
Languagept
FieldSocial Sciences
TopicAcademic Research in Diverse Fields
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsHumanitiesChemistryMaterials scienceArt

Abstract

fetched live from OpenAlex

O presente artigo tem por objetivo o desenvolvimento de rotinas automatizadas para odimensionamento de perfis de aço formados a frio visando o uso acadêmico. O mesmojustifica-se por auxiliar no desenvolvimento das atividades em disciplinas de estruturasde aço, fornecendo agilidade na resolução de exemplos. O desenvolvimento das rotinasbaseou-se na norma brasileira ABNT NBR 14762:2012, da qual foi utilizado o Método daSeção Efetiva para o dimensionamento dos perfis. Para a automatização das mesmas,utilizou-se o software matemático SMath Studio Desktop. As rotinas mostraram-se eficientes e serão implementadas como material de apoio nas disciplinas de estruturas deaço na Universidade de Passo Fundo.

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)0.022
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0170.004

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 head0.135
GPT teacher head0.383
Teacher spread0.248 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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