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A machine learning approach using partitioning around medoids clustering and random forest classification to model groups of farms in regard to production parameters and bulk tank milk antibody status of two major internal parasites in dairy cows.
Oehm, Andreas W; Springer, Andrea; Jordan, Daniela; Strube, Christina; Knubben-Schweizer, Gabriela; Jensen, Katharina Charlotte; Zablotski, Yury.
Afiliación
  • Oehm AW; Clinic for Ruminants with Ambulatory and Herd Health Services, Ludwig-Maximilians-Universität Munich, Oberschleissheim, Germany.
  • Springer A; Institute for Parasitology, Centre for Infection Medicine, University of Veterinary Medicine Hannover, Hannover, Germany.
  • Jordan D; Institute for Parasitology, Centre for Infection Medicine, University of Veterinary Medicine Hannover, Hannover, Germany.
  • Strube C; Institute for Parasitology, Centre for Infection Medicine, University of Veterinary Medicine Hannover, Hannover, Germany.
  • Knubben-Schweizer G; Clinic for Ruminants with Ambulatory and Herd Health Services, Ludwig-Maximilians-Universität Munich, Oberschleissheim, Germany.
  • Jensen KC; Clinic for Cattle, University of Veterinary Medicine Hannover, Foundation, Hannover, Germany.
  • Zablotski Y; Institute for Veterinary Epidemiology and Biostatistics, Freie Universität Berlin, Berlin, Germany.
PLoS One ; 17(7): e0271413, 2022.
Article en En | MEDLINE | ID: mdl-35816512

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Parásitos / Enfermedades de los Bovinos Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Parásitos / Enfermedades de los Bovinos Tipo de estudio: Observational_studies / Prevalence_studies / Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2022 Tipo del documento: Article País de afiliación: Alemania Pais de publicación: Estados Unidos