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Brazilian Climate Change Law

2016· book-chapter· en· W2570627165 on OpenAlexaff
Karen Alvarenga Oliveira

Bibliographic record

VenueOxford University Press eBooks · 2016
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsInternational Institute for Sustainable Development
Fundersnot available
KeywordsGreenhouse gasClimate changeDeforestation (computer science)BiomeCorporate governanceNatural resource economicsClimate change mitigationPolitical scienceOrder (exchange)GeographyBusinessEnvironmental resource managementEnvironmental planningEnvironmental protectionEconomicsFinanceEcology

Abstract

fetched live from OpenAlex

Abstract This chapter examines the climate change policy of Brazil. In 2010 at the Sixteenth Conference of Parties in Cancún, Brazil announced its voluntary national target of significantly reducing greenhouse gas (GHG) emissions between 36.1 per cent and 38.9 per cent of projected emissions by 2020. These targets were defined in the Brazilian National Policy on Climate Change (PNMC). The PNMC establishes principles, guidelines, and economic instruments for reaching the national voluntary targets. It relies on sectoral plans for mitigation and adaptation to climate change in order to facilitate the move towards a low-carbon economy. The PNMC defined various aspects related to the measurement of goals, formulation of sectoral plans and of action plans for the prevention and control of deforestation in all Brazilian biomes, and governance structure.

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)0.002
metaresearch head score (Gemma)0.005
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: Other · Consensus signal: Other
Teacher disagreement score0.038
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0280.005

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 head0.106
GPT teacher head0.216
Teacher spread0.110 · 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
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
Published2016
Admission routes1
Has abstractyes

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