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Conserv Biol. 2018 Aug;32(4):762-764. doi: 10.1111/cobi.13117.

Using machine learning to advance synthesis and use of conservation and environmental evidence.

Author information

1
National Center for Ecological Analysis and Synthesis, University of California, Santa Barbara. 735 State Street, Suite 300, Santa Barbara, CA 93101, U.S.A.
2
DataKind, 156 5th Avenue, Suit 502, New York, NY, 10010, U.S.A.
3
University of Exeter Medical School, Heavitree Road, Exeter, EX1 2LU, U.K.
4
Conservation International, 2011 Crystal Drive, Suite 500, Arlington, VA 22202, U.S.A.
5
Environmental Science and Policy, George Mason University, Fairfax, VA 22030, U.S.A.
6
European Centre for Environment and Human Health, University of Exeter, Truro, Cornwall, TR1 3HD, U.K.
7
The Nature Conservancy, 4245 Fairfax Drive, Arlington, VA 22203, U.S.A.
8
Department of Natural Resources and Environmental Sciences, University of Illinois, S-406 Turner Hall, 1102 S. Goodwin Avenue, Urbana, IL 61801, U.S.A.
9
Wildlife Conservation Society, 2300 Southern Boulevard, Bronx, NY 10460, U.S.A.
10
Social Science Research Institute, University of Hawaii, 2424 Maile Way #704, Honolulu, HI 96822, U.S.A.
11
Vulcan, Inc., 505 Fifth Avenue S, Seattle, WA 98104, U.S.A.
PMID:
29644722
DOI:
10.1111/cobi.13117

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