Articles | Volume 18, issue 6
https://doi.org/10.5194/bg-18-1941-2021
https://doi.org/10.5194/bg-18-1941-2021
Research article
 | 
19 Mar 2021
Research article |  | 19 Mar 2021

Can machine learning extract the mechanisms controlling phytoplankton growth from large-scale observations? – A proof-of-concept study

Christopher Holder and Anand Gnanadesikan

Model code and software

Dataset and scripts for manuscript "Can machine learning extract the mechanisms controlling phytoplankton growth from large-scale observations? – A proof of concept study" Christopher Holder and Anand Gnanadesikan https://doi.org/10.5281/zenodo.3932387

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Short summary
A challenge for marine ecologists in studying phytoplankton is linking small-scale relationships found in a lab to broader relationships observed on large scales in the environment. We investigated whether machine learning (ML) could help connect these small- and large-scale relationships. ML was able to provide qualitative information about the small-scale processes from large-scale information. This method could help identify important relationships from observations in future research.
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