Thomas G. Orton

1.5k total citations
54 papers, 1.1k citations indexed

About

Thomas G. Orton is a scholar working on Environmental Engineering, Soil Science and Artificial Intelligence. According to data from OpenAlex, Thomas G. Orton has authored 54 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Environmental Engineering, 23 papers in Soil Science and 13 papers in Artificial Intelligence. Recurrent topics in Thomas G. Orton's work include Soil Geostatistics and Mapping (36 papers), Soil Carbon and Nitrogen Dynamics (18 papers) and Geochemistry and Geologic Mapping (11 papers). Thomas G. Orton is often cited by papers focused on Soil Geostatistics and Mapping (36 papers), Soil Carbon and Nitrogen Dynamics (18 papers) and Geochemistry and Geologic Mapping (11 papers). Thomas G. Orton collaborates with scholars based in Australia, France and United Kingdom. Thomas G. Orton's co-authors include M. Pringle, Peter M. Kopittke, Nicolas Saby, Paul G. Dennis, Ram C. Dalal, Christian Forstner, R. M. Lark, Yash P. Dang, Neal W. Menzies and Thomas F. A. Bishop and has published in prestigious journals such as Environmental Science & Technology, The Science of The Total Environment and Scientific Reports.

In The Last Decade

Thomas G. Orton

52 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas G. Orton Australia 19 454 452 294 150 134 54 1.1k
Zhao Qiguo China 14 261 0.6× 455 1.0× 260 0.9× 226 1.5× 142 1.1× 42 1.2k
Andrew Sila Kenya 17 672 1.5× 551 1.2× 242 0.8× 202 1.3× 183 1.4× 36 1.5k
Xiangtian Meng China 19 620 1.4× 474 1.0× 556 1.9× 254 1.7× 116 0.9× 63 1.4k
Xiangrui Xu China 15 270 0.6× 271 0.6× 230 0.8× 104 0.7× 94 0.7× 31 749
Mohammad Sadegh Askari Ireland 12 278 0.6× 459 1.0× 138 0.5× 177 1.2× 51 0.4× 18 862
Calogero Schillaci Italy 20 444 1.0× 491 1.1× 293 1.0× 301 2.0× 241 1.8× 61 1.3k
Raphaël Gros France 19 182 0.4× 482 1.1× 313 1.1× 264 1.8× 223 1.7× 56 1.1k
M. Lalitha India 13 365 0.8× 450 1.0× 106 0.4× 187 1.2× 86 0.6× 55 891
Marcos Bacis Ceddia Brazil 16 368 0.8× 439 1.0× 143 0.5× 176 1.2× 79 0.6× 58 829
Yanbing Qi China 13 210 0.5× 557 1.2× 184 0.6× 189 1.3× 108 0.8× 37 1.3k

Countries citing papers authored by Thomas G. Orton

Since Specialization
Citations

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

Fields of papers citing papers by Thomas G. Orton

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas G. Orton

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas G. Orton. A scholar is included among the top collaborators of Thomas G. Orton 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 Thomas G. Orton. Thomas G. Orton 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.
2.
Voil, Peter de, Andries Potgieter, Yash P. Dang, et al.. (2024). Multimodal sequential cross-modal transformer for predicting plant available water capacity (PAWC) from time series of weather and crop biological data. Agricultural Water Management. 307. 109124–109124.
3.
Orton, Thomas G., et al.. (2023). A Study of the Relationships between Depths of Soil Constraints and Remote Sensing Data from Different Stages of the Growing Season. Remote Sensing. 15(14). 3527–3527. 1 indexed citations
4.
Orton, Thomas G., David McClymont, Kathryn Page, Neal W. Menzies, & Yash P. Dang. (2022). ConstraintID: An online software tool to assist grain growers in Australia identify areas affected by soil constraints. Computers and Electronics in Agriculture. 202. 107422–107422. 10 indexed citations
5.
Forstner, Christian, Thomas G. Orton, Adam Skarshewski, et al.. (2019). Effects of graphene oxide and graphite on soil bacterial and fungal diversity. The Science of The Total Environment. 671. 140–148. 46 indexed citations
6.
Forstner, Christian, Thomas G. Orton, Peng Wang, Peter M. Kopittke, & Paul G. Dennis. (2019). Effects of carbon nanotubes and derivatives of graphene oxide on soil bacterial diversity. The Science of The Total Environment. 682. 356–363. 26 indexed citations
7.
8.
Orton, Thomas G., et al.. (2019). Model-Based Geostatistics from a Bayesian Perspective: Investigating Area-to-Point Kriging with Small Data Sets. Mathematical Geosciences. 52(3). 397–423. 3 indexed citations
9.
Forstner, Christian, Thomas G. Orton, Peng Wang, Peter M. Kopittke, & Paul G. Dennis. (2019). Soil chloride content influences the response of bacterial but not fungal diversity to silver nanoparticles entering soil via wastewater treatment processing. Environmental Pollution. 255(Pt 2). 113274–113274. 11 indexed citations
10.
Pringle, M., et al.. (2018). An empirical model for prediction of wheat yield, using time-integrated Landsat NDVI. International Journal of Applied Earth Observation and Geoinformation. 72. 99–108. 72 indexed citations
11.
Dennis, Paul G., et al.. (2018). The effects of glyphosate, glufosinate, paraquat and paraquat-diquat on soil microbial activity and bacterial, archaeal and nematode diversity. Scientific Reports. 8(1). 2119–2119. 74 indexed citations
12.
Jones, Andrew R., Thomas G. Orton, & Ram C. Dalal. (2015). The legacy of cropping history reduces the recovery of soil carbon and nitrogen after conversion from continuous cropping to permanent pasture. Agriculture Ecosystems & Environment. 216. 166–176. 21 indexed citations
13.
Brus, D.J., et al.. (2014). Disaggregation of soil testing data on organic matter by the summary statistics approach to area-to-point kriging. Geoderma. 226-227. 151–159. 9 indexed citations
14.
Orton, Thomas G., Eva Lacarce, Jeroen Meersmans, et al.. (2014). Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale. Geoderma. 223-225. 97–107. 122 indexed citations
15.
Pringle, M., et al.. (2014). Mapping depth-to-rock from legacy data, using a generalized linear mixed model. Queensland's institutional digital repository (The University of Queensland). 295–299. 19 indexed citations
16.
Orton, Thomas G., M. Pringle, Kathryn Page, Ram C. Dalal, & Thomas F. A. Bishop. (2014). Spatial prediction of soil organic carbon stock using a linear model of coregionalisation. Geoderma. 230-231. 119–130. 32 indexed citations
17.
Orton, Thomas G., Nicolas Saby, Dominique Arrouays, et al.. (2012). Analyzing the Spatial Distribution of PCB Concentrations in Soils Using Below-Quantification Limit Data. Journal of Environmental Quality. 41(6). 1893–1905. 4 indexed citations
18.
Orton, Thomas G., Nicolas Saby, Dominique Arrouays, et al.. (2012). Spatial distribution of Lindane concentration in topsoil across France. The Science of The Total Environment. 443. 338–350. 28 indexed citations
19.
Orton, Thomas G. & R. M. Lark. (2008). The Bayesian maximum entropy method for lognormal variables. Stochastic Environmental Research and Risk Assessment. 23(3). 319–328. 7 indexed citations
20.
Parsons, David, et al.. (2004). A comparison of three modelling approaches for quantitative risk assessment using the case study of Salmonella spp. in poultry meat. International Journal of Food Microbiology. 98(1). 35–51. 31 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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