{"accessLevel": "public", "bureauCode": ["010:12"], "contactPoint": {"@type": "vcard:Contact", "fn": "Laurence Clarfeld", "hasEmail": "mailto:lclarfel@uvm.edu"}, "description": "Remote cameras (\u201ctrail cameras\u201d) are a popular tool for non-invasive, continuous wildlife monitoring, and as they become more prevalent in wildlife research, machine learning (ML) is increasingly used to automate or accelerate the labor-intensive process of labelling (i.e., tagging) photos. Human-machine hybrid tagging approaches have been shown to greatly increase tagging efficiency (i.e., time to tag a single image). However, those potential increases hinge on the extent to which an ML model makes correct vs. incorrect predictions. We performed an experiment using a ML model that produces bounding boxes around animals, people, and vehicles in remote camera imagery (MegaDetector), to consider the impact of a ML model\u2019s performance on its ability to accelerate human labeling. Six participants tagged trail camera images collected from 12 sites in Vermont and Maine, USA (January-September 2022) using three tagging methods (one with ML bounding box assistance and two without assistance).", "distribution": [{"@type": "dcat:Distribution", "accessURL": "https://doi.org/10.5066/P9FGUQEZ", "description": "Landing page for access to the data", "format": "XML", "mediaType": "application/http", "title": "Digital Data"}, {"@type": "dcat:Distribution", "description": "The metadata original format", "downloadURL": "https://data.usgs.gov/datacatalog/metadata/USGS.64da3a38d34ef477cf3edf0e.xml", "format": "XML", "mediaType": "text/xml", "title": "Original Metadata"}], "identifier": "http://datainventory.doi.gov/id/dataset/USGS_64da3a38d34ef477cf3edf0e", "keyword": ["Maine", "USGS:64da3a38d34ef477cf3edf0e", "Vermont", "biota", "camera trap", "data labelling", "machine learning", "tagging", "trail camera", "wildlife monitoring"], "modified": "2023-08-30T00:00:00Z", "publisher": {"@type": "org:Organization", "name": "U.S. Geological Survey"}, "spatial": "-73.2458, 42.8115, -67.9834, 47.1000", "theme": ["geospatial"], "title": "Evaluating a tandem human-machine approach to labelling of wildlife in remote camera monitoring"}