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.
This map shows the geographic impact of T. Strabel'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 T. Strabel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites T. Strabel more than expected).
This network shows the impact of papers produced by T. Strabel. 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 T. Strabel. The network helps show where T. Strabel may publish in the future.
Co-authorship network of co-authors of T. Strabel
This figure shows the co-authorship network connecting the top 25 collaborators of T. Strabel.
A scholar is included among the top collaborators of T. Strabel 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 T. Strabel. T. Strabel is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Pszczoła, Marcin, T. Strabel, Sebastian Mucha, & Ewa Sell‐Kubiak. (2018). Genetic architecture of methane emissions from dairy cows. Proceedings of the World Congress on Genetics Applied to Livestock Production. 477.1 indexed citations
Strabel, T., et al.. (2015). The effect of non-genetic factors on reproduction traits of primiparous Polish Holstein-Friesian cows.. Animal Science Papers and Reports. 33(4). 347–356.2 indexed citations
5.
Strabel, T., et al.. (2015). The genetic relationship between reproduction traits and milk urea concentration.. Animal Science Papers and Reports. 33(3). 243–255.5 indexed citations
Strabel, T. & J. Jamrozik. (2006). Alternative measures of lactation persistency from random regression models with Legendre polynomials.. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, 13-18 August, 2006. 1–33.3 indexed citations
Strabel, T., E. Ptak, Joanna Szyda, & J. Jamrozik. (2004). Multiple-lactation random regression test-day model for Polish Black and White cattle. Bulletin - International Bull Evaluation Service/Interbull bulletin. 133.12 indexed citations
13.
Strabel, T., E. Ptak, Joanna Szyda, & J. Jamrozik. (2004). Estimates of genetic parameters for protein yield of Polish Black-and-White cattle with multiple-lactation rendom regression test-day model. 22(2).1 indexed citations
14.
Strabel, T., Joanna Szyda, E. Ptak, & J. Jamrozik. (2003). Comparison of random regression test-day models for production traits of dairy cattle in Poland. Bulletin - International Bull Evaluation Service/Interbull bulletin. 197.6 indexed citations
Strabel, T., et al.. (2001). GENETIC EVALUATION OF PERSISTENCY IN RANDOM REGRESSION TEST DAY MODELS. Bulletin - International Bull Evaluation Service/Interbull bulletin. 27(27). 189–192.9 indexed citations
Strabel, T. & T. Szwaczkowski. (1995). Certain nongenetic effects on test-day milk yield in dairy cows. Animal Science Papers and Reports. 13. 55–64.2 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.