APOIO DA EXPERTISE CANADENSE NO DESENVOLVIMENTO DE INDICADORES DE SUSTENTABILIDADE RELACIONADOS À MARICULTURA DO BRASIL
Bibliographic record
Abstract
A ideia de Sustentabilidade tem levado as nacoes a buscar um equilibrio entre o crescimento e a protecao dos recursos naturais (Ratner, 2004). Dentro da ideia de sustentabilidade focada na maricultura, o Estado brasileiro por meio do Ministerio do Meio Ambiente, fundamentado nas recomendacoes da FAO, ja apontou diretrizes para o setor aquicola desde 1997. O objetivo e identificar as responsabilidades dos atores envolvidos com a aquicultura, sendo que o intercâmbio continuo e essencial para garantir a sustentabilidade (Eler & Millani, 2007). Apesar do conceito sustentabilidade estar internacionalmente em foco, ideias relacionadas a mensurar o que seria sustentavel ainda nao estao claras. Por este motivo, a utilizacao de indicadores que possibilitem diagnosticar a realidade local, melhorando as acoes de projetos relacionados a aquicultura, e fundamental. Ja atuando neste campo, o Canada tem investido no Atlantic Zone Monitoring Program. Esta iniciativa tem como objetivo a coleta e analise de dados para compreender a variabilidade ambiental e apoiar as atividades economicas relacionadas ao ambiente marinho (DFO, 2009). Tendo isso em vista, o presente trabalho buscou o conhecimento canadense no gerenciamento de ambientes costeiros para contribuir na selecao de indicadores de sustentabilidade relacionados a maricultura, em especial ao cultivo de ostra.
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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 | 0.034 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".