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NOVA ESTRATÉGIA NACIONAL DE DEFESA E O ALINHAMENTO DO PROGRAMA ESTRATÉGICO DO EXÉRCITO GUARANI

2019· article· pt· W2979194919 on OpenAlexaff
Luciano Luiz Goulart Silva Dias, Alzeir Costa dos Santos, Carlos Eduardo De Franciscis Ramos

Bibliographic record

VenueRevista da Escola Superior de Guerra · 2019
Typearticle
Languagept
FieldSocial Sciences
TopicEducation and Public Policy
Canadian institutionsMinistère des Transports
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

O Programa Estratégico do Exército (PrgEE) Guarani consiste na implantação de uma Nova Família de Blindados sobre Rodas (NFBR), concebida para transformar a Infantaria Motorizada em Mecanizada e modernizar as Unidades de Cavalaria, que empregam as Viaturas Blindadas de Transporte de Pessoal (VBTP) EE-11 Urutu desde 1974. Atualmente, o Programa vem contribuindo para o fomento de novas capacitações, fortalecendo a indústria brasileira com a obtenção de tecnologia de emprego dual (civil e militar). Diante do exposto, o presente trabalho discorrerá sobre o alinhamento do Projeto Viaturas 6x6, Média Sobre Rodas (MSR), com as orientações e diretrizes da minuta da Política Nacional de Defesa (PND-END 2016), tendo como base as Estratégias de Defesa (ED), que substituíram as diretrizes da Estratégia Nacional de Defesa (END 2012). Pretende-se, ao final, demonstrar que os benefícios gerados por este Programa favorecem a consecução dos Objetivos Nacionais de Defesa (OND), contribuindo para a Defesa Nacional, o desenvolvimento da Base Industrial de Defesa (BID) e a soberania do Brasil.

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)0.006
metaresearch head score (Gemma)0.007
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: none
Teacher disagreement score0.044
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.041
GPT teacher head0.353
Teacher spread0.312 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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