PRINCIPAIS CAUSAS DE CONDENAÇÕES EM VÍSCERAS COMESTÍVEIS DE SUÍNOS ABATIDOS EM UM MATADOURO FRIGORÍFICO SOB INSPEÇÃO ESTADUAL NA REGIÃO DE ALAGOINHAS-BA
Bibliographic record
Abstract
O Brasil representa o quarto maior exportador mundial de carne suina, ficando apenas atras dos EUA, Uniao Europeia e Canada. Os abates nos ultimos anos acompanharam a demanda do mercado interno e a crescente participacao do nosso pais frente ao comercio internacional, atraidos pela producao sob os servicos de inspecao federal (S.I.F.), estadual (S.I.E.), municipal (SIM) e atualmente pelo Sistema Brasileiro de Inspecao (SISBI) ja que a producao de carne e visceras de suinos devem estar de acordo com as boas praticas de fabricacao, atendendo as exigencias higienico-sanitarias e dessa maneira evitando danos a saude dos consumidores. O trabalho foi realizado em um matadouro frigorifico sob inspecao estadual, localizado na regiao de Alagoinhas no estado da Bahia. Foram avaliados 33.169 suinos, no periodo de 01 de dezembro de 2010 a 30 de abril de 2012. Do total de visceras comestiveis julgadas durante a inspecao post mortem , foram identificadas as principais causas de condenacoes, que constituiram em: pneumonia 33,25% (19.621), aspiracao de sangue 21,84% (12.887), nefrite 11,94% (7.047), uronefrose 10,96% (6.466), pleurite 5,99% (3.536), hepatite 5,03% (2.969) e pericardite 3,41% (2.013), totalizando 54.539 visceras condenadas. As maiores incidencias das rejeicoes ocorreram no mes de dezembro de 2011, assoaciado a uma maior quantidade de animais abatidos, fato comum nesse periodo do ano.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".