BibTex format
@article{Callaghan:2021:10.1038/s41558-021-01168-6,
author = {Callaghan, M and Schleussner, C-F and Nath, S and Lejeune, Q and Knutson, TR and Reichstein, M and Hansen, G and Theokritoff, E and Andrijevic, M and Brecha, RJ and Hegarty, M and Jones, C and Lee, K and Lucas, A and van, Maanen N and Menke, I and Pfleiderer, P and Yesil, B and Minx, JC},
doi = {10.1038/s41558-021-01168-6},
journal = {NATURE CLIMATE CHANGE},
pages = {966--+},
title = {Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies},
url = {http://dx.doi.org/10.1038/s41558-021-01168-6},
volume = {11},
year = {2021}
}
RIS format (EndNote, RefMan)
TY - JOUR
AU - Callaghan,M
AU - Schleussner,C-F
AU - Nath,S
AU - Lejeune,Q
AU - Knutson,TR
AU - Reichstein,M
AU - Hansen,G
AU - Theokritoff,E
AU - Andrijevic,M
AU - Brecha,RJ
AU - Hegarty,M
AU - Jones,C
AU - Lee,K
AU - Lucas,A
AU - van,Maanen N
AU - Menke,I
AU - Pfleiderer,P
AU - Yesil,B
AU - Minx,JC
DO - 10.1038/s41558-021-01168-6
EP - 966
PY - 2021///
SN - 1758-678X
SP - 966
TI - Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies
T2 - NATURE CLIMATE CHANGE
UR - http://dx.doi.org/10.1038/s41558-021-01168-6
VL - 11
ER -