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.
Countries citing papers authored by Jerome J. Pella
Since
Specialization
Citations
This map shows the geographic impact of Jerome J. Pella'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 Jerome J. Pella with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jerome J. Pella more than expected).
This network shows the impact of papers produced by Jerome J. Pella. 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 Jerome J. Pella. The network helps show where Jerome J. Pella may publish in the future.
Co-authorship network of co-authors of Jerome J. Pella
This figure shows the co-authorship network connecting the top 25 collaborators of Jerome J. Pella.
A scholar is included among the top collaborators of Jerome J. Pella 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 Jerome J. Pella. Jerome J. Pella is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Pella, Jerome J., et al.. (2021). The distribution, abundance, and ecology of larval tunas from the entrance to the Gulf of California. AquaDocs (United Nations Educational, Scientific and Cultural Organization).
2.
Garvin, Michael R., Jerome J. Pella, Patrick D. Barry, et al.. (2014). A bayesian cross-validation approach to evaluate genetic baselines and forecast the necessary number of informative single nucleotide polymorphisms.1 indexed citations
3.
Pella, Jerome J. & Jacek M. Maselko. (2007). Probability Sampling and Estimation of the Oil Remaining in 2001 from the Exxon Valdez Oil Spill in Prince William Sound, Alaska.6 indexed citations
Eiler, John H., et al.. (2004). Distribution and Movement Patterns of Chinook Salmon Returning to the Yukon River Basin in 2000-2002.16 indexed citations
Pella, Jerome J. & Michele Masuda. (2001). Bayesian methods for analysis of stock mixtures from genetic characters. Fishery Bulletin.286 indexed citations
11.
Pella, Jerome J., Michele Masuda, Charles M. Guthrie, et al.. (1998). Stock composition of some sockeye salmon, Oncorhynchus nerka, catches in Southeast Alaska, based on incidence of allozyme variants, freshwater ages, and a brain-tissue parasite. AquaDocs (United Nations Educational, Scientific and Cultural Organization).4 indexed citations
12.
Wing, Bruce L. & Jerome J. Pella. (1998). Time Series Analyses of Climatological Records from Auke Bay, Alaska.8 indexed citations
13.
Pella, Jerome J., Michele Masuda, & Sam Nelson. (1996). Search Algorithms for Computing Stock Composition of a Mixture from Traits of Individuals by Maximum Likelihood.23 indexed citations
14.
Pella, Jerome J., et al.. (1995). Incidental Catches of Salmonids in the 1991 North Pacific Squid Driftnet Fisheries.9 indexed citations
Pella, Jerome J., et al.. (1975). Measures of tuna abundance from purse-seine operations in the Eastern Pacific Ocean, adjusted for fleet-wide evolution of increased fishing power, 1960-1971. AquaDocs (United Nations Educational, Scientific and Cultural Organization).5 indexed citations
19.
Pella, Jerome J. & Patrick Tomlinson. (1969). A generalized stock production model. AquaDocs (United Nations Educational, Scientific and Cultural Organization).388 indexed citations breakdown →
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.