Machine-learning-based evidence and attribution mapping of 100,000 climate 
impact studies

Callaghan, Max; Schleussner, Carl-Friedrich; Nath, Shruti; Lejeune, Quentin; Knutson, Thomas R.; Reichstein, Markus; Hansen, Gerrit; Theokritoff, Emily; Andrijevic, Marina; Brecha, Robert J.; Hegarty, Michael; Jones, Chelsea; Lee, Kaylin; Lucas, Agathe; van Maanen, Nicole; Menke, Inga; Pfleiderer, Peter; Yesil, Burcu; Minx, Jan C.

doi:10.1038/s41558-021-01168-6

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Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies

Callaghan, M., Schleussner, C.-F., Nath, S., Lejeune, Q., Knutson, T. R., Reichstein, M., et al. (2021). Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies. Nature Climate Change, 11, 966-972. doi:10.1038/s41558-021-01168-6.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0009-573E-1 Version Permalink: https://hdl.handle.net/21.11116/0000-000A-3E95-9

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Creators:
Callaghan, Max, Author
Schleussner, Carl-Friedrich, Author
Nath, Shruti, Author
Lejeune, Quentin, Author
Knutson, Thomas R., Author
Reichstein, Markus¹, Author
Hansen, Gerrit, Author
Theokritoff, Emily, Author
Andrijevic, Marina, Author
Brecha, Robert J., Author
Hegarty, Michael, Author
Jones, Chelsea, Author
Lee, Kaylin, Author
Lucas, Agathe, Author
van Maanen, Nicole, Author
Menke, Inga, Author
Pfleiderer, Peter, Author
Yesil, Burcu, Author
Minx, Jan C., Author

Affiliations:
1Department Biogeochemical Integration, Prof. Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_1688139

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Dates: Accepted: 2021-08-31Published Online: 2021-10-11Date issued: 2021-11

Publication Status: Issued

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Identifiers: Other: BGC3716
DOI: 10.1038/s41558-021-01168-6

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Project name : USMILE

Grant ID : 855187

Funding program : Horizon 2020 (H2020)

Funding organization : European Commission (EC)

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Title: Nature Climate Change

Source Genre: Journal

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Publ. Info: London : Nature Publishing Group

Pages: - Volume / Issue: 11 Sequence Number: - Start / End Page: 966 - 972 Identifier: ISSN: 1758-678x
CoNE: https://pure.mpg.de/cone/journals/resource/1758-678x