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  Causality guided machine learning model on wetland CH4 emissions across global wetlands

Yuan, K., Zhu, Q., Li, F., Riley, W. J., Torn, M., Chu, H., et al. (2022). Causality guided machine learning model on wetland CH4 emissions across global wetlands. Agricultural and Forest Meteorology, 324: 109115. doi:10.1016/j.agrformet.2022.109115.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000B-4B47-2 Version Permalink: https://hdl.handle.net/21.11116/0000-000B-4B48-1
Genre: Journal Article

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 Creators:
Yuan, Kunxiaojia, Author
Zhu, Qing, Author
Li, Fa, Author
Riley, William J., Author
Torn, Margaret, Author
Chu, Housen, Author
McNicol, Gavin, Author
Chen, Min, Author
Knox, Sara, Author
Delwiche, Kyle, Author
Wu, Huayi, Author
Baldocchi, Dennis, Author
Ma, Hongxu, Author
Desai, Ankur R., Author
Chen, Jiquan, Author
Sachs, Torsten, Author
Ueyama, Masahito, Author
Sonnentag, Oliver, Author
Helbig, Manuel, Author
Tuittila, Eeva-Stiina, Author
Jurasinski, Gerald, AuthorKoebsch, Franziska, AuthorCampbell, David, AuthorSchmid, Hans Peter, AuthorLohila, Annalea, AuthorGöckede, Mathias1, Author           Nilsson, Mats B., AuthorFriborg, Thomas, AuthorJansen, Joachim, AuthorZona, Donatella, AuthorEuskirchen, Eugenie, AuthorWard, Eric J., AuthorBohrer, Gil, AuthorJin, Zhenong, AuthorLiu, Licheng, AuthorIwata, Hiroki, AuthorGoodrich, Jordan, AuthorJackson, Robert, Author more..
Affiliations:
1Integrating surface-atmosphere Exchange Processes Across Scales - Modeling and Monitoring, Dr. Mathias Göckede, Department Biogeochemical Signals, Prof. Dr. Sönke Zaehle, Max Planck Institute for Biogeochemistry, Max Planck Society, ou_3279725              

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 Dates: 2022-08-042022-09-15
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: Other: BGC4107
DOI: 10.1016/j.agrformet.2022.109115
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Title: Agricultural and Forest Meteorology
Source Genre: Journal
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Publ. Info: Amsterdam : Elsevier
Pages: - Volume / Issue: 324 Sequence Number: 109115 Start / End Page: - Identifier: ISSN: 0168-1923
CoNE: https://pure.mpg.de/cone/journals/resource/954928468040