Iniciativas para a redução do consumo de sódio no Brasil: avaliação e análise de impacto
Bibliographic record
Abstract
Para a minha família AGRADECIMENTOS À minha orientadora, Profa.Patrícia, profunda admiração e amizade e agradecimento pela confiança, entusiasmo e incentivo; Aos colegas do Programa de Saúde Global e Sustentabilidade; À Renata (Levy) e ao Rafael (Claro), exemplos de profissionais e companheiros de pesquisa, além de bons amigos, compartilhando materiais e bancos de pesquisas e estando sempre abertos para discutir novas ideias; Ao Everton Nunes, pelo importante apoio na discussão da adaptação da metodologia de custos atribuíveis para nutrientes críticos e nas análises de custos do sódio; Aos colegas e ex-colegas de CGAN, em especial à Michele, ex-coordenadora e grande amiga, e à Equipe de Vigilância Alimentar e Nutricional, Ana, Iracema, Rafaella e Sara, pelo profissionalismo, amizade e apoio; To my friends at the University of Liverpool, for being so open to share and teach the IMPACT methodologies, especially to Chris Kypridemos, for his genius work and willingness to help me all along the way with the IMPACT NCD BR Model; To Mary Labbé, at the University of Toronto for the fantastic work as the chair of the PAHO Technical Adisory Group for Sodium Reduction and for introducing me to the PRIME methodology at a critical moment of my research; A mis colegas de Inciensa, Costa Rica, por su coordinación excepcional del proyecto regional de IDRC, especialmente a mi gran amiga Adriana Blanco-Metlzer; Às inúmeras (os) colegas, professoras (es), e funcionárias (os) que conheci na Faculdade de Saúde Pública, por todo o suporte e trocas acadêmicas; Ao meu pai, minha maior referência de dedicação e competência acadêmica.À Vanessa, minha companheira e apoiadora em tudo.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".