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NABat ML: Utilizing deep learning to enable crowdsourced development of automated, scalable solutions for documenting North American bat populations

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作者单位: Colorado State University [1] U.S. Geological Survey [2] Upper Mississippi River National Wildlife and Fish Refuge [3] U.S. Fish and Wildlife Service [4] Wyoming Game and Fish Department [5] US Army Engineer Research & Development Center [6] National Park Service [7] Florida Fish and Wildlife Conservation Commission [8] Kansas Department of Wildlife and Parks [9] Georgia Department of Natural Resources [10] University of Nebraska Lincoln [11] Utah Division of Wildlife Resources [12] Indiana Department of Natural Resources [13] Northern Rocky Mountain Science Center [14] Fisheries, and Conservation Biology, University of Maine [15] Bat Conservation International [16] University of Edinburgh [17] Atlantic Region, Atlantic Veterinary College, University of Prince Edward Island [18] Wildlife Division, Fisheries, Forestry and Agriculture [19] Conservation Metrics, Inc. [20] University of Illinois Urbana Champaign [21] Parks Canada Agency [22] Avon Park Air Force Range [23] Bureau of Land Management [24] Canadian Wildlife Health Cooperative [25] Keweenaw Bay Indian Community Natural Resources Department [26] Montana State University [27] Pacific Southwest Research Station [28]
发布时间 2023-02-04
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The Journal of Applied Ecology

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