Using machine learning to advance synthesis and use of conservation and environmental evidence
Resource type
Journal Article
Authors/contributors
- Cheng, S. H. (Author)
- Augustin, C. (Author)
- Bethel, A. (Author)
- Gill, D. (Author)
- Anzaroot, S. (Author)
- Brun, J. (Author)
- DeWilde, B. (Author)
- Minnich, R. C. (Author)
- Garside, R. (Author)
- Masuda, Y. J. (Author)
- Miller, D. C. (Author)
- Wilkie, D. (Author)
- Wongbusarakum, S. (Author)
- McKinnon, M. C. (Author)
Title
Using machine learning to advance synthesis and use of conservation and environmental evidence
Abstract
Article impact statement: Machine learning optimizes processes of systematic evidence synthesis and improves its utility for evidence-based conservation.
Publication
Conservation Biology
Volume
32
Issue
4
Pages
762-764
Date
2018
ISSN
1523-1739
Accessed
11/09/2020, 13:17
Library Catalogue
Wiley Online Library
Citation
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. (2018). Using machine learning to advance synthesis and use of conservation and environmental evidence. Conservation Biology, 32(4), 762–764. https://doi.org/10.1111/cobi.13117
Theme
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