A framework for assessing reliability of observer annotations of aerial wildlife imagery, with insights for deep learning applications.
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| Title: | A framework for assessing reliability of observer annotations of aerial wildlife imagery, with insights for deep learning applications. |
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| Authors: | Converse RL; Center for the Advancement of Spatial Informatics Research and Education, University of New Mexico, Albuquerque, New Mexico, United States of America.; Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, New Mexico, United States of America., Lippitt CD; Center for the Advancement of Spatial Informatics Research and Education, University of New Mexico, Albuquerque, New Mexico, United States of America.; Department of Geography and Environmental Studies, University of New Mexico, Albuquerque, New Mexico, United States of America., Sesnie SE; Division of Biological Sciences, US Fish and Wildlife Southwest Regional Office, Albuquerque, New Mexico, United States of America., Harris GM; Division of Biological Sciences, US Fish and Wildlife Southwest Regional Office, Albuquerque, New Mexico, United States of America., Butler MJ; Division of Biological Sciences, US Fish and Wildlife Southwest Regional Office, Albuquerque, New Mexico, United States of America., Stewart DR; Division of Biological Sciences, US Fish and Wildlife Southwest Regional Office, Albuquerque, New Mexico, United States of America. |
| Source: | PloS one [PLoS One] 2025 Jan 15; Vol. 20 (1), pp. e0316832. Date of Electronic Publication: 2025 Jan 15 (Print Publication: 2025). |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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| ISSN: | 1932-6203 |
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| DOI: | 10.1371/journal.pone.0316832 |
