E. J. Pollak

3.8k total citations
99 papers, 2.8k citations indexed

About

E. J. Pollak is a scholar working on Genetics, Animal Science and Zoology and Agronomy and Crop Science. According to data from OpenAlex, E. J. Pollak has authored 99 papers receiving a total of 2.8k indexed citations (citations by other indexed papers that have themselves been cited), including 85 papers in Genetics, 29 papers in Animal Science and Zoology and 24 papers in Agronomy and Crop Science. Recurrent topics in E. J. Pollak's work include Genetic and phenotypic traits in livestock (76 papers), Genetic Mapping and Diversity in Plants and Animals (36 papers) and Genetics and Plant Breeding (14 papers). E. J. Pollak is often cited by papers focused on Genetic and phenotypic traits in livestock (76 papers), Genetic Mapping and Diversity in Plants and Animals (36 papers) and Genetics and Plant Breeding (14 papers). E. J. Pollak collaborates with scholars based in United States, Canada and Venezuela. E. J. Pollak's co-authors include R.L. Quaas, George Casella, Chaeyoung Lee, Vincent Ducrocq, L.D. Van Vleck, John R. Thompson, C Pélissier, H. T. Blair, Dorian J. Garrick and J. H. J. van der Werf and has published in prestigious journals such as Genetics, Biometrics and Proceedings of the Royal Society B Biological Sciences.

In The Last Decade

E. J. Pollak

92 papers receiving 2.5k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
E. J. Pollak United States 30 2.3k 947 808 591 309 99 2.8k
J. W. Wilton Canada 27 2.2k 1.0× 1.3k 1.3× 1.2k 1.5× 379 0.6× 249 0.8× 149 3.0k
M. M. de Alencar Brazil 29 1.9k 0.9× 800 0.8× 748 0.9× 552 0.9× 179 0.6× 193 2.5k
Raphael Mrode United Kingdom 26 2.0k 0.9× 1.2k 1.2× 736 0.9× 487 0.8× 344 1.1× 149 2.8k
R.L. Quaas United States 32 3.2k 1.4× 1.1k 1.1× 1.2k 1.4× 1.1k 1.8× 435 1.4× 76 3.7k
Eildert Groeneveld Germany 25 1.9k 0.9× 624 0.7× 1.2k 1.4× 290 0.5× 488 1.6× 99 2.7k
O. W. Robison United States 29 1.8k 0.8× 976 1.0× 885 1.1× 304 0.5× 602 1.9× 109 2.6k
W.F. Fikse Sweden 27 1.6k 0.7× 707 0.7× 554 0.7× 351 0.6× 357 1.2× 125 2.0k
J. K. Bertrand United States 33 2.4k 1.0× 1.0k 1.1× 1.2k 1.5× 622 1.1× 451 1.5× 115 2.8k
Daniel Sørensen Denmark 32 3.1k 1.4× 682 0.7× 858 1.1× 1.3k 2.2× 510 1.7× 88 3.8k
Peer Berg Denmark 29 1.6k 0.7× 541 0.6× 744 0.9× 474 0.8× 592 1.9× 112 2.6k

Countries citing papers authored by E. J. Pollak

Since Specialization
Citations

This map shows the geographic impact of E. J. Pollak'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 E. J. Pollak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites E. J. Pollak more than expected).

Fields of papers citing papers by E. J. Pollak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by E. J. Pollak. 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 E. J. Pollak. The network helps show where E. J. Pollak may publish in the future.

Co-authorship network of co-authors of E. J. Pollak

This figure shows the co-authorship network connecting the top 25 collaborators of E. J. Pollak. A scholar is included among the top collaborators of E. J. Pollak 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 E. J. Pollak. E. J. Pollak 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.
Rolf, Megan M, Dorian J. Garrick, Robert L Weaber, et al.. (2015). Comparison of Bayesian models to estimate direct genomic values in multi-breed commercial beef cattle. Genetics Selection Evolution. 47(1). 23–23. 36 indexed citations
2.
Lewis, Ronald M, Barbara B. Lockee, R. M. Enns, et al.. (2014). Filling the Knowledge Gap: Integrating Quantitative Genetics and Genomics in Graduate Education and Outreach. Proceedings of the World Congress on Genetics Applied to Livestock Production. 222.
3.
Drake, Daniel J., Jeremy F. Taylor, Dorian J. Garrick, et al.. (2012). The accuracies of DNA-based estimates of genetic merit derived from Angus or multibreed beef cattle training populations1,2,3. Journal of Animal Science. 90(12). 4191–4202. 7 indexed citations
4.
Pollak, E. J., et al.. (2009). Beef Symposium: The evolution of beef cattle genetic evaluation1. Journal of Animal Science. 87(suppl_14). E1–E2. 7 indexed citations
5.
Eenennaam, Alison L. Van, Robert L Weaber, Daniel J. Drake, et al.. (2007). DNA-based paternity analysis and genetic evaluation in a large, commercial cattle ranch setting1. Journal of Animal Science. 85(12). 3159–3169. 49 indexed citations
6.
Crews, D. H., et al.. (2007). Genetic evaluation of beef carcass data using different endpoint adjustments. Journal of Animal Science. 85(5). 1120–1125. 9 indexed citations
7.
Weaber, Robert L, E. J. Pollak, Dorian J. Garrick, et al.. (2006). From research to application: a model for educating beef producers in animal breeding technologies.. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, 13-18 August, 2006. 1 indexed citations
8.
Pollak, E. J.. (2006). Multibreed genetic evaluations of beef cattle in the United States.. 6 indexed citations
9.
Bullock, Kim, et al.. (2003). International beef cattle genetic evaluation in the United States and the role of the National Beef Cattle Evaluation Consortium. Bulletin - International Bull Evaluation Service/Interbull bulletin. 156. 1 indexed citations
10.
Crews, D. H., E. J. Pollak, Robert L Weaber, R.L. Quaas, & R.J. Lipsey. (2003). Genetic parameters for carcass traits and their live animal indicators in Simmental cattle1. Journal of Animal Science. 81(6). 1427–1433. 82 indexed citations
11.
Dikeman, Michael E., et al.. (2002). Heritability and Correlation Estimates of Carcass Data from Angus-Sired Steers. Iowa State University Digital Repository (Iowa State University). 1(1). 1 indexed citations
12.
Pollak, E. J., et al.. (2002). Genetic antagonism between body weight and milk production in beef cattle1. Journal of Animal Science. 80(2). 316–321. 45 indexed citations
13.
Carvalheira, J., E. J. Pollak, R.L. Quaas, & Robert W. Blake. (2002). An Autoregressive Repeatability Animal Model for Test-Day Records in Multiple Lactations. Journal of Dairy Science. 85(8). 2040–2045. 19 indexed citations
14.
15.
Lee, Chaeyoung, Curtis P. Van Tassell, & E. J. Pollak. (1997). Estimation of genetic variance and covariance components for weaning weight in Simmental cattle.. Journal of Animal Science. 75(2). 325–325. 28 indexed citations
16.
Lee, Chaeyoung & E. J. Pollak. (1997). Influence of partitioning data by sex on genetic variance and covariance components for weaning weight in beef cattle.. Journal of Animal Science. 75(1). 61–61. 27 indexed citations
17.
Lee, Chaeyoung, E. J. Pollak, R.W. Everett, & Charles E. McCulloch. (1995). Multiplicative Factors for Estimation of Daily Milk and Component Yields from Single Morning or Afternoon Tests. Journal of Dairy Science. 78(1). 221–235. 18 indexed citations
18.
Pollak, E. J.. (1988). On the theory of partially inbreeding finite populations. II. Partial sib mating.. Genetics. 120(1). 303–311. 7 indexed citations
19.
Scott, Mindy E., Darren M. Scott, & E. J. Pollak. (1988). Conditions under Which the Mean Fertility Is Maximized When a Population Is at a Stable Equilibrium.. Genetics. 118(4). 713–720.
20.
Pollak, E. J.. (1978). Design optimization of air-conditioning systems. 52(2). 123–33. 1 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.

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