Planos nacionais de controlo dos géneros alimentícios
Bibliographic record
Abstract
Este relatório descreve as atividades desenvolvidas durante o estágio curricular do curso de Mestrado Integrado em Medicina Veterinária no âmbito da inspeção sanitária e saúde pública, realizado tanto nos PIF de Lisboa, como na Divisão de Riscos Alimentares (DRA) da Autoridade de Segurança Alimentar e Económica (ASAE). Existe uma grande variedade de produtos e animais que chegam diariamente à União Europeia provenientes de diferentes países terceiros, como por exemplo a China, África do Sul, Brasil, Índia, Canadá e Senegal. Durante o período de estágio a categoria dos produtos mais inspecionada nos PIF foi o pescado representando 57,5% da totalidade das remessas inspecionadas nos portos de Lisboa e 98% no aeroporto. No âmbito do plano de fiscalização da ASAE em 2015, a aluna analisou 365 amostras de géneros alimentícios e constatou-se que o número de amostras não conformes foi 254. O grupo das bebidas alcoólicas destacou-se com 159 amostras não conformes; Abstract: National Control Plans of Food This essay describes the activities developed throughout the curricular internship of the Integrated Master of Veterinary Medicine within sanitary inspection and public health, both at border control of Lisbon as well as in the Divisão de Riscos Alimentares (DRA) of the Autoridade de Segurança Alimentar e Económica (ASAE). There is a wide variety of products and animals that arrive daily to the European Union from third countries, such as China, South Africa, Brazil, India, Canada and Senegal. During the internship the largest category of inspected food was fish representing 57.5% of the inspected remittances in the port of Lisbon and 98% in the airport. As part of the fiscalization plan of ASAE in 2015 the student analysed 365 food samples and it was found that the number of samples non-conforming was 254. The ardent spirits was the category that stood out with 159 non-conforming samples (63%).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".