Abstract On behalf of AGU, the scientific community, and the editorial team of Journal of Geophysical Research: Machine Learning and Computation, we extend our sincere gratitude to the reviewers who dedicated their time and expertise to evaluating manuscripts for us in 2025. Scientific research can now be communicated in various ways, yet peer review remains the cornerstone of scholarly publishing. We deeply appreciate the reviewers who devoted hours to reading and providing insightful feedback. The high quality of our published papers is a testament to their commitment to this vital community service.
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Enrico Camporeale
R. Marino
Thomas Berger
Journal of Geophysical Research Machine Learning and Computation
Columbia University
Centre National de la Recherche Scientifique
Chinese Academy of Sciences
Building similarity graph...
Analyzing shared references across papers
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Camporeale et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69d0aefd659487ece0fa4e9d — DOI: https://doi.org/10.1029/2026jh001360
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: