6 citations as recorded by crossref.

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2. Ways forward for Machine Learning to make useful global environmental datasets from legacy observations and measurements 10.1038/s41467-022-32693-3
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4. A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-a time series in the global ocean from physical drivers J. Roussillon et al. 10.3389/fmars.2023.1077623
5. Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset S. Stadtler et al. 10.3390/make4010008
6. Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties C. Betancourt et al. 10.5194/gmd-15-4331-2022