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Record W4386771003 · doi:10.53661/2763-686020230000007

ACESSIBILIDADE NAS ÁREAS VERDES PÚBLICAS PARA PROMOVER A JUSTIÇA AMBIENTAL

2023· article· pt· W4386771003 on OpenAlexaff
Silva Ana Cláudia Nogueira da, Angeline Martini

Bibliographic record

VenueBoletim Técnico SIF/Boletim técnico - SIF (Impresso) · 2023
Typearticle
Languagept
FieldEnvironmental Science
TopicEnvironmental Sustainability and Education
Canadian institutionsIron Ore Company (Canada)
Fundersnot available
KeywordsPolitical scienceContext (archaeology)GeographyHumanitiesArtArchaeology

Abstract

fetched live from OpenAlex

A demanda por soluções que promovam a resiliência e a sustentabilidade, sobretudo no contexto do planejamento urbano é crescente. Sendo assim, nosso objetivo é propor uma metodologia para a avaliação da acessibilidade nas áreas verdes públicas no contexto nacional, de modo a contribuir com a formulação de políticas públicas que promovam a justiça ambiental. Foi elaborado um panorama sobre os conceitos de justiça ambiental e acessibilidade às áreas verdes, bem como, um estudo de caso em Belo Horizonte, aplicando uma proposta metodológica capaz de avaliar a acessibilidade às áreas verdes da cidade. A metodologia proposta evidenciou os locais que precisam de investimentos na capital mineira, bem como, permitiu quantifi car a população afetada pela falta de acessibilidade às áreas verdes. Recomenda-se que a metodologia seja replicada nas cidades brasileiras como ferramenta de auxílio para a gestão da fl oresta urbana.

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)0.003
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 score0.063
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.028
GPT teacher head0.293
Teacher spread0.265 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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