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Record W4387935660 · doi:10.51189/coninters2023/24330

COMERCIO DE CRÉDITO DE CARBONO: ANÁLISE DE ISENÇÃO TRIBUTÁRIA COMO INCENTIVO DO DESENVOLVIMENTO SUSTENTÁVEL

2023· article· pt· W4387935660 on OpenAlexaff
Camilo Alencar Fechine Barbosa, PAOLA DIAS DA CUNHA, Patrícia de Albuquerque Sobreira, PAULO HENRIQUE SOBREIRA FRANÇA, LÍVIA CRISTINA LIMA ALMEIDA

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldEnvironmental Science
TopicUrban Arborization and Environmental Studies
Canadian institutionsCompute Canada
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Introduo: O comrcio de crdito de carbono surgiu a partir do Protocolo de Kyoto em 1997 e se caracterizou pela valorizao da preservao ambiental de forma indireta, a fim de contribuir como alternativa para o desenvolvimento sustentvel. As isenes tributrias inerentes ao crdito de carbono so a chave para gerao de riqueza atravs da compra e venda do ativo financeiro, mediante certificao adequada de sequestro de gases do efeito estufa. Objetivo: Analisar quais as isenes fiscais so inerentes ao comrcio de crdito carbono, bem como natureza fiscal e reflexos financeiros positivos s empresas participantes da transao e mitigao dos impactos climticos. Metodologia

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)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.250
Teacher spread0.231 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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