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0573979 - BÚ 2024 RIV GB eng J - Journal Article
Cai, L. - Kreft, H. - Taylor, A. - Denelle, P. - Schrader, J. - Essl, F. - van Kleunen, M. - Pergl, Jan - Pyšek, Petr - Stein, A. - Winter, M. - Barcelona, J. F. - Fuentes, N. - Inderjit, Dr. - Karger, D. N. - Kartesz, J. - Kuprijanov, A. - Nishino, M. - Nickrent, D. L. - Nowak, A. - Patzelt, A. - Pelser, P. B. - Singh, P. - Wieringa, J. J. - Weigelt, P.
Global models and predictions of plant diversity based on advanced machine learning techniques.
New Phytologist. Roč. 237, č. 4 (2023), s. 1432-1445. ISSN 0028-646X. E-ISSN 1469-8137
R&D Projects: GA ČR(CZ) GX19-28807X
Institutional support: RVO:67985939
Keywords : plant diversity * machine learning * distribution
OECD category: Ecology
Impact factor: 9.4, year: 2022
Method of publishing: Open access
https://doi.org/10.1111/nph.18533
Permanent Link: https://hdl.handle.net/11104/0344358