@article{6afeebd9128f425d8d2af36f3a3ba13b,
title = "Using machine learning to advance synthesis and use of conservation and environmental evidence",
author = "Cheng, {S. H.} and C. Augustin and A. Bethel and D. Gill and S. Anzaroot and J. Brun and B. DeWilde and Minnich, {R. C.} and R. Garside and Masuda, {Y. J.} and Miller, {D. C.} and D. Wilkie and S. Wongbusarakum and McKinnon, {M. C.}",
note = "Funding Information: We thank the Science for Nature and People Partnership (SNAPP), a partnership of The Nature Conservancy, the Wildlife Conservation Society, and the National Center for Ecological Analysis and Synthesis (NCEAS) at the University of California, Santa Barbara, for providing funding for the Evidence-Based Conservation working group. Development and deployment of Colandr was conducted in partnership with DataKind and Conservation International. We especially thank S. Sagalovsky for front-end development of Colandr and E. Fegraus at Conservation International. Finally, we thank A. Pullin, M, Balisi, A. Fritts-Penniman, and S. Bittick for providing comments on earlier drafts of this manuscript. R.G. is partially supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care (CLAHRC) for the South West Peninsula (PenCLAHRC).",
year = "2018",
month = aug,
doi = "10.1111/cobi.13117",
language = "English (US)",
volume = "32",
pages = "762--764",
journal = "Conservation Biology",
issn = "0888-8892",
publisher = "Wiley-Blackwell",
number = "4",
}