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Record W3198196886 · doi:10.5539/jsd.v14n5p74

Sustainability Indicators in Solid Waste Management: A Case Study in a Developing Country

2021· article· en· W3198196886 on OpenAlexvenueno aff
Cecilia de Mattos Canella, Fernanda Bento Rosa Gomes, Samuel Rodrigues Castro

Bibliographic record

VenueJournal of Sustainable Development · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsSocioeconomic statusMunicipal solid wasteSustainabilitySanitationBusinessWork (physics)Solid waste managementEnvironmental planningEnvironmental resource managementGeographyEnvironmental scienceEnvironmental healthPopulationWaste managementEnvironmental engineeringEngineering

Abstract

fetched live from OpenAlex

The generation of municipal solid waste (MSW) has been crescent. Due to this, there is a growing concern about MSW disposal. Whitin a scenario of limited financial resources, the management of MSW may be a challenge for Brazilian municipalities. Indicators can be effective instruments for assessing MSW management, as well as local socioeconomic and socio-environmental aspects. Thus, this work aimed at characterizing the MSW management in regions of the Brazilian state of Minas Gerais through an analysis of socioeconomic and sustainability indicators, considering the 10 years since the Brazilian National Solid Waste Policy. For this purpose, indicators of MSW management and socioeconomic development of municipalities of Minas Gerais were analyzed. The statistical analysis evidenced some characteristics reported in literature. Furthermore, MSW management indicators were not directly related to the available data on basic sanitation in the territories. Results also evidenced a lack of financial and economic sustainability of MSW management in the municipalities.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.777
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.263
Teacher spread0.254 · 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 teacher head, not a consensus.

Study designQualitative
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

Citations1
Published2021
Admission routes1
Has abstractyes

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