7 citations as recorded by crossref.

1. Marine big data-driven ensemble learning for estimating global phytoplankton group composition over two decades (1997–2020) Y. Zhang et al. https://doi.org/10.1016/j.rse.2023.113596
2. Machine learning reveals regime shifts in future ocean carbon dioxide fluxes inter-annual variability D. Couespel et al. https://doi.org/10.1038/s43247-024-01257-2
3. Can machine learning extract the mechanisms controlling phytoplankton growth from large-scale observations? – A proof-of-concept study C. Holder & A. Gnanadesikan https://doi.org/10.5194/bg-18-1941-2021
4. Using neural network ensembles to separate ocean biogeochemical and physical drivers of phytoplankton biogeography in Earth system models C. Holder et al. https://doi.org/10.5194/gmd-15-1595-2022
5. Testing the Skill of a Species Distribution Model Using a 21st Century Virtual Ecosystem L. Bardon et al. https://doi.org/10.1029/2021GL093455
6. How Well do Earth System Models Capture Apparent Relationships Between Phytoplankton Biomass and Environmental Variables? C. Holder & A. Gnanadesikan https://doi.org/10.1029/2023GB007701
7. Can three-dimensional nitrate structure be reconstructed from surface information with artificial intelligence? — A proof-of-concept study G. Yang et al. https://doi.org/10.1016/j.scitotenv.2024.171365