Novas dinâmicas territoriais no ártico: cooperação ou nova guerra fria?
Bibliographic record
Abstract
O Artico e uma das regioes do planeta mais sensiveis as mudancas climaticas e passa por drasticas mudancas ambientais, tais como a reducao da area coberta por gelo marinho. Essas mudancas, aliada a demanda por novas areas de producao de oleo e gas, transformaram o Artico numa zona de tensao politica. Apos uma breve discussao dos recursos naturais disponiveis, este artigo analisa os interesses territoriais e as acoes realizadas pelos paises com uma costa artica para explorar suas plataformas continentais e expandir suas Zonas Economicas Exclusivas (ZEE), conforme permitido pela Convencao das Nacoes Unidas sobre o Direito do Mar. Esses paises, EUA, Canada, Dinamarca (Groenlândia), Islândia, Noruega e Russia, procuram expandir suas areas geograficas de interesse e acao para o Norte, para, principalmente, explorarem os recursos naturais encontrados no assoalho oceânico. Por outro lado, o cenario politico e incerto para a regiao, principalmente se considerarmos o forte antagonismo entre os EUA e a Russia. Essa tensao foi acentuada pela recente crise na Ucrânia, o qual podera contribuir para a instabilidade das relacoes internacionais no Artico. Por outro lado, a regiao esta na pauta dos foruns internacionais, e varias nacoes nao polares, como a China e a India, ja mostram forte interesse sobre o futuro do oceano Artico e o tema comeca a vir a tona no Brasil.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".