Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Compositional Stratification in the Deep Mantle
1999564 citationsL. H. Kellogg, Bradford H. Hager et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of L. H. Kellogg's research. 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 L. H. Kellogg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites L. H. Kellogg more than expected).
This network shows the impact of papers produced by L. H. Kellogg. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by L. H. Kellogg. The network helps show where L. H. Kellogg may publish in the future.
Co-authorship network of co-authors of L. H. Kellogg
This figure shows the co-authorship network connecting the top 25 collaborators of L. H. Kellogg.
A scholar is included among the top collaborators of L. H. Kellogg based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with L. H. Kellogg. L. H. Kellogg is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Kellogg, L. H., et al.. (2018). A box model for the transport of carbon between major carbon reservoirs over geologic time.. AGU Fall Meeting Abstracts. 2018.
3.
Kreylos, Oliver & L. H. Kellogg. (2017). Immersive Visualization of the Solid Earth. AGUFM. 2017.1 indexed citations
Kreylos, Oliver, L. H. Kellogg, Sarah E. Reed, et al.. (2016). The AR Sandbox: Augmented Reality in Geoscience Education. AGU Fall Meeting Abstracts. 2016.3 indexed citations
6.
Reed, Sarah E., Oliver Kreylos, Sherry Hsi, et al.. (2014). Shaping Watersheds Exhibit: An Interactive, Augmented Reality Sandbox for Advancing Earth Science Education. AGU Fall Meeting Abstracts. 2014.41 indexed citations
7.
Weber, Christiane, Geoffrey Gebbie, Christoph Garth, et al.. (2014). A Comparison of Methods for Ocean Reconstruction from Sparse Observations. AGUFM. 2014.1 indexed citations
Yıkılmaz, M. B., et al.. (2009). New Frontiers in Fault Model Visualization and Interaction. AGUFM. 2009.2 indexed citations
14.
Kellogg, L. H., et al.. (2008). Mantle mixing and the origin and persistence of geochemical reservoirs. Geochimica et Cosmochimica Acta Supplement. 72(12).1 indexed citations
Yıkılmaz, M. B., et al.. (2007). A 3D Immersive Fault Visualizer and Editor. AGU Fall Meeting Abstracts. 2007.1 indexed citations
17.
Kreylos, Oliver, G. W. Bawden, & L. H. Kellogg. (2005). New Visualization Techniques to Analyze Ultra-High Resolution Four-dimensional Surface Deformation Imagery Collected With Ground-based Tripod LiDAR. AGUFM. 2005.1 indexed citations
18.
Moore, J. Casey, J. G. Konter, J. B. Kellogg, et al.. (2004). Scales of mantle heterogeneity. AGU Fall Meeting Abstracts. 2004.1 indexed citations
19.
Gurnis, Michael, L. H. Kellogg, Jeremy Bloxham, et al.. (2004). Computational Infrastructure for Geodynamics (CIG). AGUFM. 2004.1 indexed citations
20.
Kellogg, L. H.. (1992). MIXING IN THE MANTLE. Annual Review of Earth and Planetary Sciences. 20(1). 365–388.35 indexed citations
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.