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

Callaghan, M. W., Schleussner, C.-F., Nath, S., Lejeune, Q., Knutson, T. R., Reichstein, M., Hansen, G., Theokritoff, E., Andrijevic, M., Brecha, R. J., Hegarty, M., Jones, C., Lee, K., Lucas, A., van Maanen, N., Menke, I., Pfleiderer, P., Yesil, B., Minx, J. C. (2021): Machine-learning-based evidence and attribution mapping of 100,000 climate impact studies. - Nature Climate Change, 11, 11, 966-972.
https://doi.org/10.1038/s41558-021-01168-6

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 Creators:
Callaghan, Max W.1, Author
Schleussner, Carl-Friedrich, Author
Nath, Shruti, Author
Lejeune, Quentin, Author
Knutson, Thomas R., Author
Reichstein, Markus, 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:
1External Organizations, ou_persistent22              

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 Dates: 2021
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/s41558-021-01168-6
BibTex Citekey: RN1012
 Degree: -

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Title: Nature Climate Change
Source Genre: Journal
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Publ. Info: -
Pages: - Volume / Issue: 11 (11) Sequence Number: - Start / End Page: 966 - 972 Identifier: PIKDOMAIN: RD5 - Climate Economics and Policy - MCC Berlin
Organisational keyword: RD5 - Climate Economics and Policy - MCC Berlin
Working Group: Evidence for Climate Solutions