Climate-invariant machine learning
- Journal
- Science Advances
In The Last Decade
doi.org/10.1126/sciadv.adj7250 →Countries where authors are citing Climate-invariant machine learning
This map shows the geographic impact of Climate-invariant machine learning. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Climate-invariant machine learning with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Climate-invariant machine learning more than expected).
Fields of papers citing Climate-invariant machine learning
This network shows the impact of Climate-invariant machine learning. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Climate-invariant machine learning.
About Climate-invariant machine learning
This paper, published in 2024, received 46 indexed citations . Written by Tom Beucler, Pierre Gentine, Janni Yuval, Sungduk Yu, Stephan Rasp, Fiaz Ahmed, Paul A. O’Gorman, J. David Neelin, Nicholas J. Lutsko and Michael S. Pritchard covering the research area of Atmospheric Science and Global and Planetary Change. It is primarily cited by scholars working on Global and Planetary Change (29 citations), Atmospheric Science (28 citations) and Environmental Engineering (9 citations). Published in Science Advances.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1126/sciadv.adj7250.