12 citations as recorded by crossref.

1. Universal differential equations for glacier ice flow modelling J. Bolibar et al. 10.5194/gmd-16-6671-2023
2. Detecting the impact of climate change on alpine mass movements in observational records from the European Alps M. Jacquemart et al. 10.1016/j.earscirev.2024.104886
3. Evaluating the affecting factors of glacier mass balance in Tanggula Mountains using explainable machine learning and the open global glacier model Q. Xu et al. 10.1007/s11629-024-9047-4
4. Twentieth century temperature and snow cover changes in the French Alps J. Beaumet et al. 10.1007/s10113-021-01830-x
5. EisNet: Extracting Bedrock and Internal Layers From Radiostratigraphy of Ice Sheets With Machine Learning S. Dong et al. 10.1109/TGRS.2021.3136648
6. Multiproxy tree ring reconstruction of glacier mass balance: insights from Pinus cembra trees growing near Silvretta Glacier (Swiss Alps) J. Lopez-Saez et al. 10.5194/cp-20-1251-2024
7. Interpreting Deep Machine Learning for Streamflow Modeling Across Glacial, Nival, and Pluvial Regimes in Southwestern Canada S. Anderson & V. Radić 10.3389/frwa.2022.934709
8. Last Glacial Maximum Climate and Glacial Scale Affected by the Monsoon Inferred from Reconstructing the Tianchi Area, Changbai Mountains, Eastern China H. Zhao & W. Zhang 10.3390/app14073019
9. Nonlinear sensitivity of glacier mass balance to future climate change unveiled by deep learning J. Bolibar et al. 10.1038/s41467-022-28033-0
10. Artificial intelligence achieves easy-to-adapt nonlinear global temperature reconstructions using minimal local data M. Wegmann & F. Jaume-Santero 10.1038/s43247-023-00872-9
11. Automated Delineation of Supraglacial Debris Cover Using Deep Learning and Multisource Remote Sensing Data S. Kaushik et al. 10.3390/rs14061352
12. A deep learning reconstruction of mass balance series for all glaciers in the French Alps: 1967–2015 J. Bolibar et al. 10.5194/essd-12-1973-2020