Scott Sievert
Impact in
- Agronomy and Crop Science top 2%
- Ruminant Nutrition and Digestive Physiology
- Reproductive Physiology in Livestock
- Agronomic Practices and Intercropping Systems
- Bioenergy crop production and management
- Forestry top 10%
Papers in
-
- Ruminant Nutrition and Digestive Physiology 4
- Milk Quality and Mastitis in Dairy Cows 1
-
- Mobile Crowdsensing and Crowdsourcing 2
- Co-authors
- R.D. ShaverP.C. HoffmanD.K. CombsArvind SatyanarayanBrian GrangerDominik MoritzJeffrey HeerKanit Wongsuphasawat
- Journals
- Journal of Dairy Science (3 papers)Journal of Animal Science (1 paper)Journal of Nonverbal Behavior (1 paper)2022 IEEE International Conference on Big Data (Big Data) (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesCanadaIsrael
In The Last Decade
Scott Sievert
12 papers receiving 413 citations
Peers
Comparison fields: 5 of 102
- Agronomy and Crop Science 241
- Forestry 28
- Animal Science and Zoology 39
- Environmental Chemistry 38
- Genetics 79
Countries citing papers authored by Scott Sievert
This map shows the geographic impact of Scott Sievert'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 Scott Sievert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scott Sievert more than expected).
Fields of papers citing papers by Scott Sievert
This network shows the impact of papers produced by Scott Sievert. 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 Scott Sievert. The network helps show where Scott Sievert may publish in the future.
Co-authors
The 25 scholars most cited alongside Scott Sievert, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 1 | |
| 2 | 2022 | 1 | |
| 3 | 2022 | 1 | |
| 4 | 2021 | 15 | |
| 5 | 2021 | 5 | |
| 6 | 2019 | 7 | |
| 7 | 2018 | 126 | |
| 8 | ATOMO: Communication-efficient Learning via Atomic Sparsification | 2018 | 31 |
| 9 | 2017 | 5 | |
| 10 | 1993 | 193 | |
| 11 | 1993 | 18 | |
| 12 | 1993 | 37 | |
| 13 | Effects of nonfiber carbohydrate and Aspergillus oryzae fermentation extract on intake, milk production, and digestion in lactating dairy cows. | 1990 | 5 |
About Scott Sievert
Scott Sievert is a scholar working on Agronomy and Crop Science, Computer Science Applications, Artificial Intelligence, Human-Computer Interaction and Management Science and Operations Research, having authored 13 papers that have together received 445 indexed citations. Recurring topics across this work include Ruminant Nutrition and Digestive Physiology (4 papers), Machine Learning and Data Classification (2 papers), Genetic and phenotypic traits in livestock (2 papers), Stochastic Gradient Optimization Techniques (2 papers), Data Stream Mining Techniques (2 papers), Advanced Bandit Algorithms Research (2 papers), Mobile Crowdsensing and Crowdsourcing (2 papers) and Milk Quality and Mastitis in Dairy Cows (1 paper). The work is most often cited by research in Agronomy and Crop Science (241 citations), Forestry (28 citations), Animal Science and Zoology (39 citations), Environmental Chemistry (38 citations) and Genetics (79 citations). Scott Sievert has collaborated with scholars based in United States, Canada and Israel. Frequent co-authors include R.D. Shaver, P.C. Hoffman, D.K. Combs, Arvind Satyanarayan, Brian Granger, Dominik Moritz, Jeffrey Heer, Kanit Wongsuphasawat, Jake Vanderplas and Shengchao Liu. Their work appears in journals such as Journal of Dairy Science, Journal of Animal Science, Journal of Nonverbal Behavior, 2022 IEEE International Conference on Big Data (Big Data) and arXiv (Cornell University).
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.