Use of deep learning to predict chronic wasting disease status based on animal movement.
Saved in:
| Title: | Use of deep learning to predict chronic wasting disease status based on animal movement. |
|---|---|
| Authors: | Blaha RL; Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi State, MS, 39762, USA., Silva CJ; United States Department of Agriculture, Agriculture Research Service, Western Regional Research Center, Albany, CA, 94710, USA., Cunningham SA; Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi State, MS, 39762, USA.; Alexander Center for Applied Population Biology, Lincoln Park Zoo, Chicago, IL, 60614, USA., DeVivo MT; Department of Veterinary Sciences, University of Wyoming, Laramie, WY, 82070, USA.; Washington Department of Fish and Wildlife, Spokane Valley, Washington, USA., Edmunds DR; Department of Veterinary Sciences, University of Wyoming, Laramie, WY, 82070, USA.; Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, 80523, USA., Boudreau MR; Department of Wildlife, Fisheries and Aquaculture, Mississippi State University, Mississippi State, MS, 39762, USA. mel.r.boudreau@gmail.com. |
| Source: | Movement ecology [Mov Ecol] 2026 Jun 10. Date of Electronic Publication: 2026 Jun 10. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: BioMed Central Country of Publication: England NLM ID: 101635009 Publication Model: Print-Electronic Cited Medium: Print ISSN: 2051-3933 (Print) Linking ISSN: 20513933 NLM ISO Abbreviation: Mov Ecol |
| Database: | MEDLINE Ultimate |
Be the first to leave a comment!
