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

Article impact statement: Machine learning optimizes processes of systematic evidence synthesis and improves its utility for evidence‐based conservation.

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Bibliographic Details
Published inConservation biology Vol. 32; no. 4; pp. 762 - 764
Main Authors Cheng, S.H., Augustin, C., Bethel, A., Gill, D., Anzaroot, S., Brun, J., DeWilde, B., Minnich, R.C., Garside, R., Masuda, Y.J., Miller, D.C., Wilkie, D., Wongbusarakum, S., McKinnon, M.C.
Format Journal Article
LanguageEnglish
Published United States Wiley Blackwell, Inc 01.08.2018
Blackwell Publishing Ltd
Subjects
Artificial intelligence
Conservation
Environmental impact
Learning algorithms
Letter
Machine learning
Synthesis
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Summary:Article impact statement: Machine learning optimizes processes of systematic evidence synthesis and improves its utility for evidence‐based conservation.
Bibliography:Machine learning optimizes processes of systematic evidence synthesis and improves its utility for evidence‐based conservation.
Article impact statement
SourceType-Other Sources-1
ObjectType-Article-2
content type line 63
ObjectType-Correspondence-1
ISSN:0888-8892
1523-1739
DOI:10.1111/cobi.13117