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Record W4417060405 · doi:10.63330/aurumpub.017-009

ECOPONTOS COMO FERRAMENTA DE SUSTENTABILIDADE: UMA REVISÃO CRÍTICA ENTRE BRASIL E PAÍSES DE REFERÊNCIA

2025· book-chapter· W4417060405 on OpenAlexaboutno aff
Camila Garcia Gonçalves, Luís Eduardo Tavares Martins, Pascal Silas Thue, Érico Kunde Corrêa, Luciara Bilhalva Corrêa

Bibliographic record

Venuenot available
Typebook-chapter
Language
FieldEnvironmental Science
TopicEnvironmental Sustainability and Education
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Work (physics)Quarter (Canadian coin)

Abstract

fetched live from OpenAlex

A crescente geração de Resíduos Sólidos Urbanos (RSU) representa um desafio global, associado ao crescimento populacional, padrões de consumo e urbanização intensa. No Brasil, apesar da Política Nacional de Resíduos Sólidos (Lei 12.305/2010), persistem limitações técnicas, orçamentárias e de adesão às diretrizes, comprometendo a gestão. Nesse contexto, os pontos de entrega voluntária (PEVs), também chamados de ecopontos, surgem como instrumentos para subsidiar a coleta seletiva e a valorização de materiais. Este estudo, de abordagem quali-quantitativa e caráter exploratório, baseia-se em revisão de literatura científica e análise documental para avaliar a efetividade dos ecopontos no Brasil em comparação com países internacionais. Os resultados indicam posição desfavorável em relação a países da União Europeia, mas desempenho intermediário frente aos blocos emergentes, destaca-se pela inclusão de catadores informais e cooperativas de reciclagem. Conclui-se que no Brasil, são necessários investimentos contínuos, planejamentos estratégicos e maior participação social, a fim de ampliar o potencial desses instrumentos de sustentabilidade e inclusão social.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, 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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.681
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.1720.003

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 head0.013
GPT teacher head0.256
Teacher spread0.243 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2025
Admission routes1
Has abstractyes

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