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Record W2130254602 · doi:10.11606/rdg.v27i0.509

A RELEVÂNCIA DA CRIAÇÃO DE UMA UNIDADE DE CONSERVAÇÃO NO MORRO GAÚCHO, MUNICÍPIOS DE ARROIO DO MEIO E CAPITÃO/RS

2014· article· pt· W2130254602 on OpenAlexaff
Bruna Letícia Thomas, Pedro Augusto Thomas, Eliane Maria Foleto

Bibliographic record

VenueGeography Department University of Sao Paulo · 2014
Typearticle
Languagept
FieldEnvironmental Science
TopicEnvironmental Sustainability and Education
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesGeographyArt

Abstract

fetched live from OpenAlex

Unidades de Conservação (UCs) são áreas definidas geograficamente vinculadas a processos de gestão de seu território, tendo como objetivo principal a proteção ao longo prazo dos atributos ambientais in situ. Assim, visa-se destacar a importância da criação de uma UC no Morro Gaúcho, patrimônio natural dos municípios de Arroio do Meio e Capitão, Rio Grande do Sul, a partir da identificação dos impactos ambientais que ameaçam a sua biodiversidade e paisagem. Desta forma, o trabalho ocorreu a partir de saídas de campo, leitura de pesquisas já realizadas na área e jornais locais e a participação em reuniões de uma organização não-governamental local. Os impactos ambientais reconhecidos foram a ocorrência de espécies exóticas invasoras, o turismo irregular e a presença de uma unidade de transbordo de resíduos sólidos, entre outros, tendo como consequências as agressões à biodiversidade local. A importância da criação de uma UC no local também é destacada pela presença da Reserva da Biosfera e os percentuais de remanescentes de Mata Atlântica nas áreas dos municípios. Portanto, a instituição de uma área protegida no Morro Gaúcho visa garantir a conservação a partir de uma ação concreta de proteção à natureza e seus processos de manejo e gestão vinculados.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.085
Threshold uncertainty score0.169

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.005
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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 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
Published2014
Admission routes1
Has abstractyes

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