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Record W2464210507 · doi:10.21438/rbgas.030401

Principais deficiências dos estudos de impacto ambiental

2016· article· pt· W2464210507 on OpenAlexaff
Alexandre Nascimento de Almeida, Nathália Barbosa de Oliveira, João Carlos Garzel Leodoro da Silva, Humberto Ângelo

Bibliographic record

VenueRevista Brasileira de Gestão Ambiental e Sustentabilidade · 2016
Typearticle
Languagept
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsImpact
Fundersnot available
KeywordsPolitical scienceHumanitiesPhysicsBusinessPhilosophy

Abstract

fetched live from OpenAlex

O licenciamento ambiental de atividades que causem impacto significativo exige a elaboração do Estudo de Impacto Ambiental (EIA) e o seu respectivo Relatório de Impacto Ambiental (RIMA). Porém, esses estudos tem apresentado uma baixa qualidade, que contribui para tornar o processo de licenciamento ambiental lento e pouco efetivo. O objetivo do trabalho é identificar as principais deficiências dos EIAs a partir da opinião de analistas ambientais do Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis (IBAMA) de Brasília. A amostra contou com a percepção de 53 analistas e o método empregado foi a análise de conteúdo. Os resultados indicaram que os principais problemas ocorrem nas etapas do diagnóstico ambiental e na análise dos impactos. Em menor quantidade, foram realizadas críticas em relação à comunicação dos EIAs e na elaboração dos termos de referência para realização dos estudos. A partir dos resultados, pode-se concluir que os problemas nos EIAs decorrem de dificuldades de coordenação e integração dos diferentes estudos relacionados ao meio ambiente, bem como, da falta de entendimento das funções e objetivos dos EIAs.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.005

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 head0.018
GPT teacher head0.284
Teacher spread0.266 · 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 designObservational
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

Citations12
Published2016
Admission routes1
Has abstractyes

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