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Record W2998634700 · doi:10.17271/1980082715420192204

Avaliação de impactos ambientais na microbacia do rio Inhandava

2019· article· pt· W2998634700 on OpenAlexaff
Evanisa Fátima Reginato Quevedo Melo, Rodrigo Henryque Reginato Quevedo Melo, Pietra Taize Bueno

Bibliographic record

VenuePeriódico Eletrônico Fórum Ambiental da Alta Paulista. · 2019
Typearticle
Languagept
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsImpact
Fundersnot available
KeywordsEnvironmental scienceGeographyPolitical science

Abstract

fetched live from OpenAlex

As bacias hidrográficas são ecossistemas importantes para avaliação dos impactos causados pela atividade antrópica, os quais acarretam riscos ao equilíbrio e a manutenção da qualidade ambiental. Com o objetivo de avaliar os impactos sofridos pela microbacia do Rio Inhandava criou-se uma adaptação da matriz de interação e da de Leopold para verificar os impactos que afetam a qualidade e equilíbrio do recurso hídrico. Para isso, foi realizado o diagnóstico das possíveis causas que alteram a qualidade do rio e avaliado quanto a sua magnitude e importância. O método utilizado tem como finalidade elencar as atividades mais impactantes possibilitando a identificação das situações críticas contribuindo para a melhor gestão ambiental da área, servindo também como fonte para tomada de decisão para a aplicação de ações corretivas e mitigadoras dos impactos verificados. A matriz é uma ferramenta para otimizar o processo de gestão e identificação das atividades causadoras de impactos, possibilitando identificar os parâmetros de maior influência, sejam positivos ou negativos. Verificou-se que uma das maneiras mais eficazes de garantir a qualidade do rio Inhandava é através do licenciamento ambiental. PALAVRAS-CHAVE: Rio Inhandava. Matriz de Leopold. Impacto Ambiental.

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), Scholarly communication, 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0500.034

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 head0.016
GPT teacher head0.278
Teacher spread0.261 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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