COMPETITIVIDADE NO MERCADO MUNDIAL DE CARNE SUÍNA: 2007-2017
Bibliographic record
Abstract
Grupo de Trabalho (GT): << 01.Mercados agrícolas e comércio exterior>> Resumo O objetivo principal do artigo é estudar o desempenho da competitividade no mercado mundial de carne suína, total e por subgrupos, no período 2007-2017.A metodologia é pautada em análises de indicadores de competitividade tradicionais e um indicador de competitividade global.Os dados provêm das Nações Unidas e OECD.Da análise da competitividade global, concluiu-se que Dinamarca, Espanha e Brasil são países altamente competitivos no setor.Bélgica, Canadá e Países Baixos possuem competitividade média e Estados Unidos baixa competitividade.Outrossim, demonstrou-se que há diferenças na competitividade entre os subgrupos de carne suína.A UE tem hegemonia no subgrupo de carnes frescas ou refrigeradas, enquanto o Brasil lidera o segmento de congelados.Barreiras sanitárias e fitossanitárias; bem-estar e saúde animal; rastreabilidade e questões ambientais são desafios às exportações do Brasil nos mercados da Ásia, UE e Rússia.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".