Caleb M Shull

721 total citations
48 papers, 423 citations indexed

About

Caleb M Shull is a scholar working on Animal Science and Zoology, Small Animals and Genetics. According to data from OpenAlex, Caleb M Shull has authored 48 papers receiving a total of 423 indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Animal Science and Zoology, 25 papers in Small Animals and 11 papers in Genetics. Recurrent topics in Caleb M Shull's work include Animal Nutrition and Physiology (29 papers), Animal Behavior and Welfare Studies (25 papers) and Effects of Environmental Stressors on Livestock (14 papers). Caleb M Shull is often cited by papers focused on Animal Nutrition and Physiology (29 papers), Animal Behavior and Welfare Studies (25 papers) and Effects of Environmental Stressors on Livestock (14 papers). Caleb M Shull collaborates with scholars based in United States, Brazil and Italy. Caleb M Shull's co-authors include Christian Maltecca, Clint Schwab, Francesco Tiezzi, Nathan P. McNulty, Constantino Schillebeeckx, Duc Lu, M. Ellis, Justin Fix, Matteo Bergamaschi and A. C. Dilger and has published in prestigious journals such as SHILAP Revista de lepidopterología, Scientific Reports and Journal of Animal Science.

In The Last Decade

Caleb M Shull

44 papers receiving 418 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Caleb M Shull United States 11 271 168 153 110 62 48 423
Anouschka Middelkoop Netherlands 12 233 0.9× 181 1.1× 100 0.7× 43 0.4× 55 0.9× 18 400
Justin Fix United States 13 329 1.2× 254 1.5× 90 0.6× 281 2.6× 27 0.4× 25 539
Mohammad Borhan Al‐Zghoul Jordan 13 367 1.4× 56 0.3× 82 0.5× 55 0.5× 67 1.1× 37 529
Jun-Kyu Son South Korea 10 153 0.6× 86 0.5× 81 0.5× 126 1.1× 38 0.6× 33 368
A. Dirkzwager Netherlands 9 257 0.9× 110 0.7× 64 0.4× 112 1.0× 23 0.4× 13 440
Mukund A. Kataktalware India 11 192 0.7× 116 0.7× 42 0.3× 84 0.8× 17 0.3× 44 351
Viktoria Neubauer Austria 15 145 0.5× 112 0.7× 113 0.7× 133 1.2× 26 0.4× 28 555
Dave J Seymour Canada 7 123 0.5× 129 0.8× 51 0.3× 123 1.1× 28 0.5× 31 348
Clothilde Villot France 9 73 0.3× 132 0.8× 117 0.8× 39 0.4× 95 1.5× 20 352
Mariana Boscato Menegat United States 8 171 0.6× 138 0.8× 41 0.3× 25 0.2× 23 0.4× 28 277

Countries citing papers authored by Caleb M Shull

Since Specialization
Citations

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

Fields of papers citing papers by Caleb M Shull

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Caleb M Shull. 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 Caleb M Shull. The network helps show where Caleb M Shull may publish in the future.

Co-authorship network of co-authors of Caleb M Shull

This figure shows the co-authorship network connecting the top 25 collaborators of Caleb M Shull. A scholar is included among the top collaborators of Caleb M Shull 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 Caleb M Shull. Caleb M Shull 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.
Tiezzi, Francesco, Clint Schwab, Caleb M Shull, & Christian Maltecca. (2024). Multiple‐trait genomic prediction for swine meat quality traits using gut microbiome features as a correlated trait. Journal of Animal Breeding and Genetics. 142(1). 102–117. 1 indexed citations
2.
Shull, Caleb M, et al.. (2023). Analysis of polygenic selection in purebred and crossbred pig genomes using generation proxy selection mapping. Genetics Selection Evolution. 55(1). 62–62. 1 indexed citations
3.
Shull, Caleb M, et al.. (2023). 187 Effect of Piglet Weaning Weight on Wean-to-Finish Growth Performance and Ultrasound Carcass Measures. Journal of Animal Science. 101(Supplement_2). 9–10. 5 indexed citations
4.
Shull, Caleb M, et al.. (2023). 180 Effect of Providing Liquid Milk Replacer During Lactation to Litters of Different Size on Piglet Pre-Weaning Performance. Journal of Animal Science. 101(Supplement_2). 1–1.
5.
Lourenço, Daniela, Christian Maltecca, Justin Fix, et al.. (2022). Genotyping and phenotyping strategies for genetic improvement of meat quality and carcass composition in swine. Genetics Selection Evolution. 54(1). 42–42. 7 indexed citations
6.
Maltecca, Christian, Jicai Jiang, Justin Fix, et al.. (2022). 406. Compressing microbiota information using an autoencoder to predict growth traits in swine. 1692–1695. 1 indexed citations
7.
Shull, Caleb M, et al.. (2022). 3 Effect of Litter Size and Provision of Supplementary Liquid Milk Replacer During Lactation on Piglet Pre-Weaning Performance. Journal of Animal Science. 100(Supplement_2). 2–3. 3 indexed citations
8.
Macciotta, N.P.P., Matteo Bergamaschi, Christian Maltecca, et al.. (2021). Genetic Parameters for Tolerance to Heat Stress in Crossbred Swine Carcass Traits. Frontiers in Genetics. 11. 612815–612815. 16 indexed citations
9.
Maltecca, Christian, Robert R. Dunn, Yuqing He, et al.. (2021). Microbial composition differs between production systems and is associated with growth performance and carcass quality in pigs. SHILAP Revista de lepidopterología. 3(1). 57–57. 9 indexed citations
10.
Shull, Caleb M, et al.. (2021). 186 Effects of a Phytogenic Feed Additive (Aromex® Pro) and Narasin (Skycis®) on Finishing Pig Growth Performance and Carcass Characteristics. Journal of Animal Science. 99(Supplement_1). 87–88. 1 indexed citations
11.
Shull, Caleb M, et al.. (2021). Effect of drying and warming piglets at birth on preweaning mortality. Translational Animal Science. 5(1). txab016–txab016. 11 indexed citations
12.
Gates, Richard S., et al.. (2021). Effects of drying and providing supplemental oxygen to piglets at birth on rectal temperature over the first 24 h after birth. Translational Animal Science. 5(3). txab095–txab095. 6 indexed citations
13.
Bergamaschi, Matteo, Christian Maltecca, Constantino Schillebeeckx, et al.. (2020). Heritability and genome-wide association of swine gut microbiome features with growth and fatness parameters. Scientific Reports. 10(1). 10134–10134. 52 indexed citations
14.
Tiezzi, Francesco, Justin Fix, Clint Schwab, Caleb M Shull, & Christian Maltecca. (2020). Gut microbiome mediates host genomic effects on phenotypes: a case study with fat deposition in pigs. Computational and Structural Biotechnology Journal. 19. 530–544. 20 indexed citations
15.
Maltecca, Christian, Duc Lu, Constantino Schillebeeckx, et al.. (2019). Predicting Growth and Carcass Traits in Swine Using Microbiome Data and Machine Learning Algorithms. Scientific Reports. 9(1). 6574–6574. 45 indexed citations
16.
Lu, Duc, Francesco Tiezzi, Constantino Schillebeeckx, et al.. (2018). Host contributes to longitudinal diversity of fecal microbiota in swine selected for lean growth. Microbiome. 6(1). 4–4. 103 indexed citations
17.
Shull, Caleb M, et al.. (2018). 45 Increased Lysine: ME ratio improves grower pig performance during a PRRSV challenge.. Journal of Animal Science. 96(suppl_2). 24–25. 1 indexed citations
18.
Ellis, M. A., et al.. (2017). 216 Effect of a post-weaning supplemental nutrition program on the growth performance, and morbidity and mortality of nursery pigs. Journal of Animal Science. 95(suppl_2). 103–104. 2 indexed citations
19.
20.
Shull, Caleb M. (2013). Modeling growth of pigs reared to heavy weights. 9 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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