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Record W4387082798 · doi:10.29327/1298891.4-301

NAVIOS, TANQUES E AERONAVES DE GUERRA: ANÁLISE DA APLICAÇÃO DAS TECNOLOGIAS DA INDÚSTRIA 4.0

2023· article· pt· W4387082798 on OpenAlexaff
Rilkemberg Fernandes Santos, Wanderson Falcão Ribeiro, Luanderson Gyuseppe da Silva Anjos, Cipriano Pereira de Melo Neto, Mateus da Silva Lemos, Zulmara Virgínia De Carvalho

Bibliographic record

VenueAnais do Congresso Brasileiro Interdisciplinar em Ciência e Tecnologia. · 2023
Typearticle
Languagept
FieldSocial Sciences
TopicAcademic Research in Diverse Fields
Canadian institutionsImpact
Fundersnot available
KeywordsMaterials scienceComputer sciencePhysicsHumanitiesArt

Abstract

fetched live from OpenAlex

Resumo: O paradigma tecnológico 4.0 trouxe diversas mudanças para o cenário global, desde a capacidade de gerir e gerar dados e processá-los, como nortear megatendências em vários setores da economia.Essa transformação digital impactou diretamente toda a indústria, modificando seus modelos de atuação e processos.Alicerçado em seus pilares: big data, realidade aumentada, impressão 3D, computação em nuvem, robôs autônomos, inteligência artificial, internet das coisas, cibersegurança, integração de sistemas e nanotecnologia, essas tecnologias habilitadoras promovem a disseminação da digitalização e a tecnologia da informação.Desta forma analisou-se os principais players requerentes de patentes, como as principais aplicações, seus inventores e países, no âmbito internacional e no território nacional para tanques, navios e aviões de guerra. Palavras-chave: transformação

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)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.353

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.005
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.113
GPT teacher head0.432
Teacher spread0.319 · 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 designNot applicable
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 abstractno

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