Thank You to Our Peer Reviewers in 2024

Enrico Camporeale; Raffaele Marino; Thomas Berger; Yangkang Chen; Doris Folini; Geoffrey Fox; Xiaofeng Li; Donald Lucas; Steve Tobias; Markus Reichstein; John Rundle; Chaopeng Shen; Renata Wentzcovitch; Yixin Wen

doi:10.1029/2025JH000681

Thank You to Our Peer Reviewers in 2024

Enrico Camporeale
, Raffaele Marino
, Thomas Berger
, Yangkang Chen
, Doris Folini
, Geoffrey Fox
, Xiaofeng Li
, Donald Lucas
, Steve Tobias
, Markus Reichstein
, John Rundle
, Chaopeng Shen
, Renata Wentzcovitch
, Yixin Wen

Queen Mary University of London
University of Colorado Boulder
École centrale de Lyon
University of Texas at Austin
Swiss Federal Institute of Technology Zurich
University of Virginia School of Engineering and Applied Science
CAS - Institute of Oceanology
Lawrence Livermore National Laboratory
University of Leeds
Max Planck Institute for Biogeochemistry
University of California at Davis
Pennsylvania State University
Columbia University
University of Florida

Research output: Contribution to journal › Editorial

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 2024. 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.

Original language	English
Article number	e2025JH000681
Journal	Journal of Geophysical Research: Machine Learning and Computation
Volume	2
Issue number	1
DOIs	https://doi.org/10.1029/2025JH000681
State	Published - Mar 2025
Externally published	Yes

Keywords

editorial
peer review

Access to Document

10.1029/2025JH000681

Cite this

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title = "Thank You to Our Peer Reviewers in 2024",

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 2024. 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.",

keywords = "editorial, peer review",

author = "Enrico Camporeale and Raffaele Marino and Thomas Berger and Yangkang Chen and Doris Folini and Geoffrey Fox and Xiaofeng Li and Donald Lucas and Steve Tobias and Markus Reichstein and John Rundle and Chaopeng Shen and Renata Wentzcovitch and Yixin Wen",

note = "Publisher Copyright: {\textcopyright} 2025. The Author(s). Journal of Geophysical Research: Machine Learning and Computation published by Wiley Periodicals LLC on behalf of American Geophysical Union.",

year = "2025",

month = mar,

doi = "10.1029/2025JH000681",

language = "English",

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T1 - Thank You to Our Peer Reviewers in 2024

AU - Camporeale, Enrico

AU - Marino, Raffaele

AU - Berger, Thomas

AU - Chen, Yangkang

AU - Folini, Doris

AU - Fox, Geoffrey

AU - Li, Xiaofeng

AU - Lucas, Donald

AU - Tobias, Steve

AU - Reichstein, Markus

AU - Rundle, John

AU - Shen, Chaopeng

AU - Wentzcovitch, Renata

AU - Wen, Yixin

PY - 2025/3

Y1 - 2025/3

N2 - 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 2024. 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.

AB - 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 2024. 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.

KW - editorial

KW - peer review

UR - https://www.scopus.com/pages/publications/105030176315

U2 - 10.1029/2025JH000681

DO - 10.1029/2025JH000681

M3 - Editorial

AN - SCOPUS:105030176315

SN - 2993-5210

VL - 2

JO - Journal of Geophysical Research: Machine Learning and Computation

JF - Journal of Geophysical Research: Machine Learning and Computation

IS - 1

M1 - e2025JH000681

ER -

Thank You to Our Peer Reviewers in 2024

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Keywords

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