G.E. Shook

3.7k total citations · 1 hit paper
70 papers, 2.9k citations indexed

About

G.E. Shook is a scholar working on Genetics, Agronomy and Crop Science and Epidemiology. According to data from OpenAlex, G.E. Shook has authored 70 papers receiving a total of 2.9k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Genetics, 36 papers in Agronomy and Crop Science and 12 papers in Epidemiology. Recurrent topics in G.E. Shook's work include Genetic and phenotypic traits in livestock (32 papers), Milk Quality and Mastitis in Dairy Cows (25 papers) and Mycobacterium research and diagnosis (12 papers). G.E. Shook is often cited by papers focused on Genetic and phenotypic traits in livestock (32 papers), Milk Quality and Mastitis in Dairy Cows (25 papers) and Mycobacterium research and diagnosis (12 papers). G.E. Shook collaborates with scholars based in United States, Egypt and Canada. G.E. Shook's co-authors include Brian Kirkpatrick, G.R. Wiggans, Michael T. Collins, M.M. Schutz, Richard F. Raubertas, Michael Gonda, Georgios Banos, K.A. Weigel, Yu‐Ming Chang and L.H. Schultz and has published in prestigious journals such as PLoS ONE, Journal of Dairy Science and Journal of Animal Science.

In The Last Decade

G.E. Shook

68 papers receiving 2.7k citations

Hit Papers

An Optimum Transformation... 1980 2026 1995 2010 1980 100 200 300 400 500

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
G.E. Shook 2.0k 1.7k 571 455 380 70 2.9k
G. de Jong 1.4k 0.7× 2.0k 1.2× 870 1.5× 597 1.3× 84 0.2× 110 2.7k
D.A. Todhunter 2.1k 1.0× 519 0.3× 477 0.8× 621 1.4× 106 0.3× 75 3.6k
B. Heringstad 2.5k 1.2× 2.8k 1.6× 780 1.4× 616 1.4× 44 0.1× 148 3.6k
A.R. Cromie 1.3k 0.6× 1.5k 0.9× 710 1.2× 304 0.7× 100 0.3× 95 2.3k
E. Ezra 1.4k 0.7× 1.7k 1.0× 494 0.9× 185 0.4× 38 0.1× 77 2.4k
Eveline M. Ibeagha‐Awemu 887 0.4× 1.2k 0.7× 287 0.5× 85 0.2× 131 0.3× 102 2.6k
Mauricio A. Elzo 761 0.4× 1.4k 0.8× 887 1.6× 201 0.4× 54 0.1× 165 2.3k
K.M. Moyes 1.3k 0.7× 554 0.3× 381 0.7× 297 0.7× 56 0.1× 48 1.7k
R. Jorritsma 1.5k 0.8× 1.0k 0.6× 487 0.9× 424 0.9× 70 0.2× 59 2.1k
F.C. Cardoso 1.5k 0.8× 579 0.3× 429 0.8× 297 0.7× 53 0.1× 90 2.1k

Countries citing papers authored by G.E. Shook

Since Specialization
Citations

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

Fields of papers citing papers by G.E. Shook

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of G.E. Shook

This figure shows the co-authorship network connecting the top 25 collaborators of G.E. Shook. A scholar is included among the top collaborators of G.E. Shook 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 G.E. Shook. G.E. Shook 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.
Zare, Yalda, et al.. (2018). A positional candidate gene association analysis of susceptibility to paratuberculosis on bovine chromosome 7. Infection Genetics and Evolution. 65. 163–169. 8 indexed citations
2.
Zare, Yalda, et al.. (2017). An across-breed genome wide association analysis of susceptibility to paratuberculosis in dairy cattle. Journal of Dairy Research. 84(1). 61–67. 21 indexed citations
3.
Zare, Yalda, G.E. Shook, Michael T. Collins, & Brian Kirkpatrick. (2014). Short communication: Heritability estimates for susceptibility to Mycobacterium avium subspecies paratuberculosis infection defined by ELISA and fecal culture test results in Jersey cattle. Journal of Dairy Science. 97(7). 4562–4567. 17 indexed citations
4.
Kirkpatrick, Brian & G.E. Shook. (2011). Genetic Susceptibility to Paratuberculosis. Veterinary Clinics of North America Food Animal Practice. 27(3). 559–571. 19 indexed citations
5.
Gonda, Michael, Yu‐Ming Chang, G.E. Shook, Michael T. Collins, & Brian Kirkpatrick. (2007). Effect of Mycobacterium paratuberculosis infection on production, reproduction, and health traits in US Holsteins. Preventive Veterinary Medicine. 80(2-3). 103–119. 73 indexed citations
6.
Gonda, Michael, Yu‐Ming Chang, G.E. Shook, Michael T. Collins, & Brian Kirkpatrick. (2006). Genetic Variation of Mycobacterium avium ssp. paratuberculosis Infection in US Holsteins. Journal of Dairy Science. 89(5). 1804–1812. 93 indexed citations
7.
Zaitoun, Ismail, et al.. (2006). Effects of the Signal Transducer and Activator of Transcription 1 (STAT1) Gene on Milk Production Traits in Holstein Dairy Cattle. Journal of Dairy Science. 89(11). 4433–4437. 57 indexed citations
8.
Shook, G.E.. (2006). Major Advances in Determining Appropriate Selection Goals. Journal of Dairy Science. 89(4). 1349–1361. 161 indexed citations
9.
Caraviello, D.Z., K.A. Weigel, G.E. Shook, & P.L. Ruegg. (2005). Assessment of the Impact of Somatic Cell Count on Functional Longevity in Holstein and Jersey Cattle Using Survival Analysis Methodology. Journal of Dairy Science. 88(2). 804–811. 43 indexed citations
10.
Shook, G.E., et al.. (2005). The Effect of Synchronization on Genetic Parameters of Reproductive Traits in Dairy Cattle. Journal of Dairy Science. 88(6). 2217–2225. 20 indexed citations
11.
Shook, G.E. & P.L. Ruegg. (1999). Geometric mean somatic cell counts: what they are; what they do.. 93–100. 5 indexed citations
12.
Khan, M. S. & G.E. Shook. (1996). Effects of Age on Milk Yield: Time Trends and Method of Adjustment. Journal of Dairy Science. 79(6). 1057–1064. 17 indexed citations
13.
Dado, R.G., G.E. Shook, & D.R. Mertens. (1994). Nutrient Requirements and Feed Costs Associated with Genetic Improvement in Production of Milk Components. Journal of Dairy Science. 77(2). 598–608. 10 indexed citations
14.
Shook, G.E., et al.. (1990). Field study of high and average producing Wisconsin dairy herds.. Journal of Dairy Science. 73. 1 indexed citations
15.
Dado, R.G., D.R. Mertens, & G.E. Shook. (1990). Theoretical metabolizable energy and protein requirements for milk component production.. Journal of Dairy Science. 73. 2 indexed citations
16.
Shook, G.E., et al.. (1982). Genetic parameters for lactation average of somatic cell concentration in milk. 142–147. 22 indexed citations
17.
Shook, G.E., et al.. (1980). Heritability and repeatability of somatic cell concentration in milk.. Journal of Dairy Science. 63. 9 indexed citations
18.
Shook, G.E., et al.. (1980). Correction factors for somatic cell concentration in milk.. Journal of Dairy Science. 63. 2 indexed citations
19.
Shook, G.E., L.P. Johnson, & F.N. Dickinson. (1975). Bias and precision of several sampling schemes for estimating lactation milk yield. Journal of Dairy Science. 58(5). 772. 4 indexed citations
20.
Shook, G.E., et al.. (1974). Survey of coaches and contestants participating in the 1973 World Dairy Expo dairy cattle judging contest. Journal of Dairy Science. 57(5). 640. 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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