Leveraging productivity indicators for anomaly detection in swine breeding herds with unsupervised learning.

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Bibliographic Details
Title: Leveraging productivity indicators for anomaly detection in swine breeding herds with unsupervised learning.
Authors: Pedro Mil-Homens M; Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States.; Department of Animal Health and Anatomy, Faculty of Veterinary Medicine, Autonomous University of Barcelona, Barcelona, Spain., Wang C; Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States.; Department of Statistics, College of Liberal Arts and Sciences, Iowa State University, Ames, IA, United States., Trevisan G; Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States., Dórea F; Swedish Veterinary Agency, Uppsala, Sweden.; Food and Agriculture Organization of the United Nations, FAO, Rome, Lazio, Italy., Linhares DCL; Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States., Holtkamp D; Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States., Silva GS; Department of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA, United States.
Source: Frontiers in veterinary science [Front Vet Sci] 2025 Nov 17; Vol. 12, pp. 1586438. Date of Electronic Publication: 2025 Nov 17 (Print Publication: 2025).
Publication Type: Journal Article
Journal Info: Publisher: Frontiers Media S.A Country of Publication: Switzerland NLM ID: 101666658 Publication Model: eCollection Cited Medium: Print ISSN: 2297-1769 (Print) Linking ISSN: 22971769 NLM ISO Abbreviation: Front Vet Sci Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2297-1769
DOI:10.3389/fvets.2025.1586438