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Volume 10, issue 9 | Copyright
Geosci. Model Dev., 10, 3519-3545, 2017
https://doi.org/10.5194/gmd-10-3519-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 25 Sep 2017

Development and technical paper | 25 Sep 2017

Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming

Iulia Ilie et al.
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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer review completion
AR: Author's response | RR: Referee report | ED: Editor decision
AR by Iulia Ilie on behalf of the Authors (10 Apr 2017)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (24 Apr 2017) by Sandra Arndt
RR by Anonymous Referee #1 (12 Jun 2017)
ED: Reconsider after major revisions (13 Jun 2017) by Sandra Arndt
AR by Iulia Ilie on behalf of the Authors (31 Jul 2017)  Author's response    Manuscript
ED: Publish as is (21 Aug 2017) by Sandra Arndt
Publications Copernicus
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Short summary
Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
Accurate representation of land-atmosphere carbon fluxes is essential for future climate...
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