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Record W2892315608 · doi:10.17648/rsd-v7i8.382

Resíduo Urbano em Portugal – Uma Análise dos Resíduos Plástico

2018· article· pt· W2892315608 on OpenAlexfundno aff
Márcio Magera Conceição, Joelma Telesi Pacheco Conceição, Paula Castro

Bibliographic record

VenueResearch Society and Development · 2018
Typearticle
Languagept
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
FundersFederation for the Humanities and Social Sciences
KeywordsHumanitiesGeographyPolitical sciencePhysicsArt

Abstract

fetched live from OpenAlex

O presente artigo trata das políticas públicas no tratamento e destino correto do resíduo plástico em Portugal, e as consequências que estes resíduos podem provocar nos mares a onde é o destino de grande parte depois do uso. O plástico em Portugal é um dos itens com menor índice de reciclagem nas cinco características dos resíduos domésticos urbanos, ficando atrás de muitos países da Comunidade Europeia que possuem um índice superior a 42% apresentado pelos Portugueses, conforme Ponto Verde, 2018. O artigo também direciona o melhor tratamento destes resíduos e como deve ser aproveitado para gerar emprego e renda a milhares de pessoas, ao invés de ser destinado a aterros sanitários ou jogado nos mares através dos rios.

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)0.001
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 score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.098
GPT teacher head0.355
Teacher spread0.257 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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