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Registro Completo |
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Biblioteca(s): |
Embrapa Soja. |
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Data corrente: |
17/07/2006 |
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Data da última atualização: |
05/10/2007 |
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Autoria: |
BENASSI, V. de T. |
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Título: |
O programa "Soja na Mesa". |
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Ano de publicação: |
2006 |
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Fonte/Imprenta: |
In: CONGRESO DE SOJA DEL MERCOSUR, 3., 2006, Rosário. Mercosoja 2006: conferencias plenarias, foros, workshops. Rosário: Associación de la Cadena de Soja Argentina, 2006. |
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Páginas: |
p. 224-227. |
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Idioma: |
Português |
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Thesagro: |
Nutrição Humana; Soja. |
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Categoria do assunto: |
-- |
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Marc: |
LEADER 00493naa a2200145 a 4500
001 1469457
005 2007-10-05
008 2006 bl uuuu u00u1 u #d
100 1 $aBENASSI, V. de T.
245 $aO programa "Soja na Mesa".
260 $c2006
300 $ap. 224-227.
650 $aNutrição Humana
650 $aSoja
773 $tIn: CONGRESO DE SOJA DEL MERCOSUR, 3., 2006, Rosário. Mercosoja 2006: conferencias plenarias, foros, workshops. Rosário: Associación de la Cadena de Soja Argentina, 2006.
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Registro original: |
Embrapa Soja (CNPSO) |
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Registro Completo
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Biblioteca(s): |
Embrapa Pecuária Sudeste. |
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Data corrente: |
18/03/2024 |
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Data da última atualização: |
18/03/2024 |
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Tipo da produção científica: |
Artigo em Periódico Indexado |
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Circulação/Nível: |
A - 2 |
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Autoria: |
NICIURA, S. C. M.; SANCHES, G. M. |
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Afiliação: |
SIMONE CRISTINA MEO NICIURA, CPPSE; GUILHERME MARTINELI SANCHES, Universidade de São Paulo. |
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Título: |
Machine learning prediction of multiple anthelmintic resistance and gastrointestinal nematode control in sheep flocks. |
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Ano de publicação: |
2024 |
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Fonte/Imprenta: |
Revista Brasileira de Parasitologia Veterinária, v. 33, n. 1, jan./mar. 2024. |
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DOI: |
10.1590/S1984-29612024014 |
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Idioma: |
Inglês |
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Conteúdo: |
The high prevalence of Haemonchus contortus and its anthelmintic resistance have affected sheep production worldwide. Machine learning approaches are able to investigate the complex relationships among the factors involved in resistance. Classification trees were built to predict multidrug resistance from 36 management practices in 27 sheep flocks. Resistance to five anthelmintics was assessed using a fecal egg count reduction test (FECRT), and 20 flocks with FECRT < 80% for four or five anthelmintics were considered resistant. The data were randomly split into training (75%) and test (25%) sets, resampled 1,000 times, and the classification trees were generated for the training data. Of the 1,000 trees, 24 (2.4%) showed 100% accuracy, sensitivity, and specificity in predicting a flock as resistant or susceptible for the test data. Forage species was a split common to all 24 trees, and the most frequent trees (12/24) were split by forage species, grazing pasture area, and fecal examination. The farming system, Suffolk sheep breed, and anthelmintic choice criteria were practices highlighted in the other trees. These management practices can be used to predict the anthelmintic resistance status and guide measures for gastrointestinal nematode control in sheep flocks. |
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Palavras-Chave: |
Machine learning; Multidrug resistance; Random forest. |
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Thesaurus NAL: |
Carts; Gastrointestinal nematodes. |
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Categoria do assunto: |
H Saúde e Patologia |
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URL: |
https://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1162930/1/MachineLearningPrediction.pdf
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Marc: |
LEADER 01977naa a2200205 a 4500
001 2162930
005 2024-03-18
008 2024 bl uuuu u00u1 u #d
024 7 $a10.1590/S1984-29612024014$2DOI
100 1 $aNICIURA, S. C. M.
245 $aMachine learning prediction of multiple anthelmintic resistance and gastrointestinal nematode control in sheep flocks.$h[electronic resource]
260 $c2024
520 $aThe high prevalence of Haemonchus contortus and its anthelmintic resistance have affected sheep production worldwide. Machine learning approaches are able to investigate the complex relationships among the factors involved in resistance. Classification trees were built to predict multidrug resistance from 36 management practices in 27 sheep flocks. Resistance to five anthelmintics was assessed using a fecal egg count reduction test (FECRT), and 20 flocks with FECRT < 80% for four or five anthelmintics were considered resistant. The data were randomly split into training (75%) and test (25%) sets, resampled 1,000 times, and the classification trees were generated for the training data. Of the 1,000 trees, 24 (2.4%) showed 100% accuracy, sensitivity, and specificity in predicting a flock as resistant or susceptible for the test data. Forage species was a split common to all 24 trees, and the most frequent trees (12/24) were split by forage species, grazing pasture area, and fecal examination. The farming system, Suffolk sheep breed, and anthelmintic choice criteria were practices highlighted in the other trees. These management practices can be used to predict the anthelmintic resistance status and guide measures for gastrointestinal nematode control in sheep flocks.
650 $aCarts
650 $aGastrointestinal nematodes
653 $aMachine learning
653 $aMultidrug resistance
653 $aRandom forest
700 1 $aSANCHES, G. M.
773 $tRevista Brasileira de Parasitologia Veterinária$gv. 33, n. 1, jan./mar. 2024.
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Embrapa Pecuária Sudeste (CPPSE) |
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