Avelar, S., van der Voort, T. S., and Eglinton, T. I.: Relevance of carbon stocks of marine sediments for national greenhouse gas inventories of maritime nations, Carbon Balance and Management, 12, 10, https://doi.org/10.1186/s13021-017-0077-x, 2017.
Bauer, J. E., Cai, W.-J., Raymond, P. A., Bianchi, T. S., Hopkinson, C. S., and Regnier, P. A. G.: The changing carbon cycle of the coastal ocean, Nature, 504, 61–70, https://doi.org/10.1038/nature12857, 2013.
Berner, R. A.: Burial of organic carbon and pyrite sulfur in the modern ocean; its geochemical and environmental significance, Am. J. Sci., 282, 451–473, https://doi.org/10.2475/ajs.282.4.451, 1982.
Burdige, D. J.: Preservation of Organic Matter in Marine Sediments: Controls, Mechanisms, and an Imbalance in Sediment Organic Carbon Budgets?, Chem. Rev., 107, 467–485, https://doi.org/10.1021/cr050347q, 2007.
Cavan, E. L. and Hill, S. L.: Commercial fishery disturbance of the global ocean biological carbon sink, Glob. Change Biol., 28, 1212–1221, https://doi.org/10.1111/gcb.16019, 2022.
Clare, M. A., Lichtschlag, A., Paradis, S., and Barlow, N. L. M.: Assessing the impact of the global subsea telecommunications network on sedimentary organic carbon stocks, Nat. Commun., 14, 2080, https://doi.org/10.1038/s41467-023-37854-6, 2023.
Copernicus: Arctic Ocean Wave Hindcast – ARCTIC_MULTIYEAR_WAV_002_013 – Norwegia
n Meteorological Institute, Copernicus Marine Data Store [data set], https://doi.org/10.48670/moi-00008, 2022a.
Copernicus: Global Ocean Colour (GlobColour) – ACRI – OCEANCOLOUR_GLO_BGC_L3_MY_009_103 – Bio-Geo-Chemical, L3 (daily) from Satellite Observations (1997–ongoing), Copernicus Marine Data Store [data set], https://doi.org/10.48670/moi-00280, 2022b.
Copernicus: Global Ocean Waves Reanalysis – WAVERYS – GLOBAL_MULTIYEAR_WAV_001_032 – Mercator Océan International, Copernicus Marine Data Store [data set], https://doi.org/10.48670/moi-00022, 2022c.
DFO: Federal Marine Bioregions, Fisheries and Oceans Canada, Open Canada [data set], Record ID: 23eb8b56-dac8-4efc-be7c-b8fa11ba62e9, 2022.
Diesing, M., Kroger, S., Parker, R., Jenkins, C., Mason, C., and Weston, K.: Predicting the standing stock of organic carbon in surface sediments of the North-West European continental shelf, Biogeochemistry, 135, 183–200, https://doi.org/10.1007/s10533-017-0310-4, 2017.
Diesing, M., Thorsnes, T., and Bjarnadóttir, L. R.: Organic carbon densities and accumulation rates in surface sediments of the North Sea and Skagerrak, Biogeosciences, 18, 2139–2160, https://doi.org/10.5194/bg-18-2139-2021, 2021.
Diesing, M., Paradis, S., Jensen, H., Thorsnes, T., Bjarnadóttir, L. R., and Knies, J.: Organic Carbon Stocks and Accumulation Rates in Surface Sediments of the Norwegian Continental Margin, ESS Open Archive [preprint], https://doi.org/10.22541/essoar.170067250.09972865/v1, 2023.
Duarte, C. M., Middelburg, J. J., and Caraco, N.: Major role of marine vegetation on the oceanic carbon cycle, Biogeosciences, 2, 1–8, https://doi.org/10.5194/bg-2-1-2005, 2005.
Enkin, J. R.: Sediment Grain Size Distribution Measurements, from Canadian Pacific Seafloor Samples, Collected from 1951 to 2017, NRCan Open S&T Repository (OSTR) [data set], in press, 2024.
Epstein, G. and Roberts, C. M.: Identifying priority areas to manage mobile bottom fishing on seabed carbon in the UK, PLOS Climate, 1, e0000059, https://doi.org/10.1371/journal.pclm.0000059, 2022.
Epstein, G. and Roberts, C. M.: Does biodiversity-focused protection of the seabed deliver carbon benefits? A U.K. case study, Conserv. Lett., 16, e12929, https://doi.org/10.1111/conl.12929, 2023.
Epstein, G., Middelburg, J. J., Hawkins, J. P., Norris, C. R., and Roberts, C. M.: The impact of mobile demersal fishing on carbon storage in seabed sediments, Glob. Change Biol., 28, 2875–2894, https://doi.org/10.1111/gcb.16105, 2022.
Epstein, G., Fuller, S. D., Hingmire, D., Myers, P., Peña, A., Pennelly, C., and Baum, J. K.: Predictive maps and related data on organic carbon stocks in surficial sediments of the Canadian continental margin, Borealis V1 [data set], https://doi.org/10.5683/SP3/ICHVVA, 2024.
Evans, J. S. and Murphy, M. A.: spatialEco, R package version 1.3-6, CRAN [code],
https://github.com/jeffreyevans/spatialEco (last access: 24 November 2022), 2021.
Flanders Marine Institute: Boundies of Canda EEZ – mrgid 8493, Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11, https://doi.org/10.14284/386, 2019.
GEBCO: GEBCO Compilation Group – GEBCO_2022 Grid, The General Bathymetric Chart of the Oceans, https://doi.org/10.5285/e0f0bb80-ab44-2739-e053-6c86abc0289c, 2022.
Gräler, B., Pebesma, E., and Heuvelink, G.: Spatio-Temporal Interpolation using gstat, R J., 8, 204–218, https://doi.org/10.32614/RJ-2016-014, 2016.
Graw, J. H., Wood, W. T., and Phrampus, B. J.: Predicting Global Marine Sediment Density Using the Random Forest Regressor Machine Learning Algorithm, J. Geophys. Res.-Sol. Ea., 126, e2020JB020135, https://doi.org/10.1029/2020JB020135, 2021.
Gregr, E. J., Haggarty, D. R., Davies, S. C., Fields, C., and Lessard, J.: Comprehensive marine substrate classification applied to Canada's Pacific shelf, PLOS ONE, 16, 1–28, https://doi.org/10.1371/journal.pone.0259156, 2021.
Gudmundsson, L., Bremnes, J. B., Haugen, J. E., and Engen-Skaugen, T.: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations – a comparison of methods, Hydrol. Earth Syst. Sci., 16, 3383–3390, https://doi.org/10.5194/hess-16-3383-2012, 2012.
Halpern, B. S., Frazier, M., Afflerbach, J., Lowndes, J. S., Micheli, F., O'Hara, C., Scarborough, C., and Selkoe, K. A.: Recent pace of change in human impact on the world's ocean, Sci. Rep., 9, 11609, https://doi.org/10.1038/s41598-019-47201-9, 2019.
Hiddink, J. G., van de Velde, S. J., McConnaughey, R. A., De Borger, E., Tiano, J., Kaiser, M. J., Sweetman, A. K., and Sciberras, M.: Quantifying the carbon benefits of ending bottom trawling, Nature, 617, E1–E2, https://doi.org/10.1038/s41586-023-06014-7, 2023.
Hijmans, R. J.: terra: Spatial Data Analysis, R package version 1.5-21, CRAN [code],
https://CRAN.R-project.org/package=terra (last access: 24 November 2022), 2022.
Hilborn, R. and Kaiser, M. J.: A path forward for analysing the impacts of marine protected areas, Nature, 607, E1–E2, https://doi.org/10.1038/s41586-022-04775-1, 2022.
Hoegh-Guldberg, O., Lovelock, C., Caldeira, K., Howard, J., Chopin, T., and Gaines, S.: The ocean as a solution to climate change: five opportunities for action, World Resources Institute, Washington, DC,
http://www.oceanpanel.org/climate (last access: 4 July 2023), 2019.
Hu, X., Myers, P. G., and Lu, Y.: Pacific Water Pathway in the Arctic Ocean and Beaufort Gyre in Two Simulations With Different Horizontal Resolutions, J. Geophys. Res.-Oceans, 124, 6414–6432, https://doi.org/10.1029/2019JC015111, 2019.
Hülse, D., Arndt, S., Wilson, J. D., Munhoven, G., and Ridgwell, A.: Understanding the causes and consequences of past marine carbon cycling variability through models, Earth-Sci. Rev., 171, 349–382, https://doi.org/10.1016/j.earscirev.2017.06.004, 2017.
Ilich, A. R., Misiuk, B., Lecours, V., and Lecours, S. A.: MultiscaleDTM, Zenodo [code], https://doi.org/10.5281/zenodo.5548338, 2021.
IPCC: 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, edited by: Calvo Buendia, E., Tanabe, K., Kranjc, A., Baasansuren, J., Fukuda, M., Ngarize, S., Osako, A., Pyrozhenko, Y., Shermanau, P., and Federici, S., IPCC, Switzerland, 2019.
Jenkins, C. J.: Summary of the onCALCULATION methods used in dbSEABED, in: usSEABED: Gulf of Mexico and Caribbean (Puerto Rico and U.S. Virgin Islands) Offshore Surficial Sediment Data Release: U.S, edited by: Buczkowski, B. J., Reid, J. A., Jenkins, C. J., Reid, J. M., Williams, S. J., and Flocks, J. G., United States Geological Survey, Series 146, version 1.0,
http://pubs.usgs.gov/ds/2006/146/ (last access: 24 November 2022), 2005.
Keil, R.: Anthropogenic Forcing of Carbonate and Organic Carbon Preservation in Marine Sediments, Annu. Rev. Mar. Sci., 9, 151–172, https://doi.org/10.1146/annurev-marine-010816-060724, 2017.
Kuhn, M.: Building Predictive Models in R Using the caret Package, J. Stat. Softw., 28, 1–26, https://doi.org/10.18637/jss.v028.i05, 2008.
Kuhn, M. and Wickham, H.: Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles, CRAN [code],
https://cran.r-project.org/package=tidymodels (last access: 24 November 2022), 2020.
Kuzyk, Z. Z. A., Gobeil, C., Goñi, M. A., and Macdonald, R. W.: Early diagenesis and trace element accumulation in North American Arctic margin sediments, Geochim. Cosmochim. Ac., 203, 175–200, https://doi.org/10.1016/j.gca.2016.12.015, 2017.
LaRowe, D. E., Arndt, S., Bradley, J. A., Burwicz, E., Dale, A. W., and Amend, J. P.: Organic carbon and microbial activity in marine sediments on a global scale throughout the Quaternary, Geochim. Cosmochim. Ac., 286, 227–247, https://doi.org/10.1016/j.gca.2020.07.017, 2020a.
LaRowe, D. E., Arndt, S., Bradley, J. A., Estes, E. R., Hoarfrost, A., Lang, S. Q., Lloyd, K. G., Mahmoudi, N., Orsi, W. D., Shah Walter, S. R., Steen, A. D., and Zhao, R.: The fate of organic carbon in marine sediments – New insights from recent data and analysis, Earth-Sci. Rev., 204, https://doi.org/10.1016/j.earscirev.2020.103146, 2020b.
Lee, T. R., Wood, W. T., and Phrampus, B. J.: A Machine Learning (kNN) Approach to Predicting Global Seafloor Total Organic Carbon, Global Biogeochem. Cy., 33, 37–46, https://doi.org/10.1029/2018gb005992, 2019.
Ludwig, M., Moreno-Martinez, A., Hölzel, N., Pebesma, E., and Meyer, H.: Assessing and improving the transferability of current global spatial prediction models, Global Ecol. Biogeogr., 32, 356–368, https://doi.org/10.1111/geb.13635, 2023.
Luisetti, T., Turner, R. K., Andrews, J. E., Jickells, T. D., Kröger, S., Diesing, M., Paltriguera, L., Johnson, M. T., Parker, E. R., Bakker, D. C. E., and Weston, K.: Quantifying and valuing carbon flows and stores in coastal and shelf ecosystems in the UK, Ecosystem Services, 35, 67–76, https://doi.org/10.1016/j.ecoser.2018.10.013, 2019.
Luisetti, T., Ferrini, S., Grilli, G., Jickells, T. D., Kennedy, H., Kröger, S., Lorenzoni, I., Milligan, B., van der Molen, J., Parker, R., Pryce, T., Turner, R. K., and Tyllianakis, E.: Climate action requires new accounting guidance and governance frameworks to manage carbon in shelf seas, Nat. Commun., 11, 4599, https://doi.org/10.1038/s41467-020-18242-w, 2020.
Macreadie, P. I., Costa, M. D. P., Atwood, T. B., Friess, D. A., Kelleway, J. J., Kennedy, H., Lovelock, C. E., Serrano, O., and Duarte, C. M.: Blue carbon as a natural climate solution, Nat. Rev. Earth Environ., 2, 826–839, https://doi.org/10.1038/s43017-021-00224-1, 2021.
Madec, G., Delecluse, P., Imbard, M., and Lévy, C.: OPA 8.1Ocean General Circulation Model, Technical Report of LODYC/IPSL, Note 11,
https://www.nemo-ocean.eu/wp-content/uploads/Doc_OPA8.1.pdf (last access: 2 Feburary 2023), 1998.
Mahoney, M. J., Johnson, L. K., Silge, J., Frick, H., Kuhn, M., and Beier, C. M.: Assessing the performance of spatial cross-validation approaches for models of spatially structured data, arXiv [preprint], https://doi.org/10.48550/arXiv.2303.07334, 2023.
Martin, K. M., Wood, W. T., and Becker, J. J.: A global prediction of seafloor sediment porosity using machine learning, Geophys. Res. Lett., 42, 10640–10646, https://doi.org/10.1002/2015GL065279, 2015.
Masson, D. and Fine, I.: Modeling seasonal to interannual ocean variability of coastal British Columbia, J. Geophys. Res.-Oceans, 117, C10019, https://doi.org/10.1029/2012JC008151, 2012.
Maxwell, A. E. and Shobe, C. M.: Land-surface parameters for spatial predictive mapping and modeling, Earth-Sci. Rev., 226, 103944, https://doi.org/10.1016/j.earscirev.2022.103944, 2022.
Meyer, H. and Pebesma, E.: Machine learning-based global maps of ecological variables and the challenge of assessing them, Nat. Commun., 13, 2208, https://doi.org/10.1038/s41467-022-29838-9, 2022.
Meyer, H., Reudenbach, C., Wöllauer, S., and Nauss, T.: Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction, Ecol. Modell., 411, 108815, https://doi.org/10.1016/j.ecolmodel.2019.108815, 2019.
Meyer, H., Milà, C., and Ludwig, M.: CAST: “caret” Applications for Spatial-Temporal Models, R package version 0.7.1, CRAN [code],
https://CRAN.R-project.org/package=CAST (last access: 24 November 2022), 2023.
Microsoft Corporation and Weston, S.: doParallel: Foreach Parallel Adaptor for the “parallel” Package, R package version 1.0.17, CRAN [code],
https://cran.r-project.org/web/packages/doParallel/index.html (last access: 24 November 2022), 2022.
Middelburg, J. J.: Reviews and syntheses: to the bottom of carbon processing at the seafloor, Biogeosciences, 15, 413–427, https://doi.org/10.5194/bg-15-413-2018, 2018.
Middelburg, J. J.: Marine Carbon Biogeochemistry: A Primer for Earth System Scientists, Springer, Cham, Switzerland, 118 pp., ISBN 978-3-030-10822-9, 2019.
Mitchell, P. J., Aldridge, J., and Diesing, M.: Legacy Data: How Decades of Seabed Sampling Can Produce Robust Predictions and Versatile Products, Geosciences, 9, 182, https://doi.org/10.3390/geosciences9040182, 2019.
Molnar, C., Bischl, B., and Casalicchio, G.: iml: An R package for Interpretable Machine Learning, JOSS, 3, 786, https://doi.org/10.21105/joss.00786, 2018.
Nellemann, C., Corcoran, E., Duarte, C. M., Valdés, L., De Young, C., Fonseca, L., and Grimsditch, G.: Blue Carbon: A Rapid Response Assessment, United Nations Environment Programme, GRID-Arendal, Norway,
https://wedocs.unep.org/20.500.11822/7772, 2009.
NRCan: Lakes, Rivers and Glaciers in Canada – Hydrographic Features – Natural Resources Canada, Topographic Data of Canada – CanVec Series, Open Canada [data set], Record ID: 9d96e8c9-22fe-4ad2-b5e8-94a6991b744b, 2019.
NRCan: Canada west coast topo-bathymetric digital elevation model – Natural Resources Canada/Department of Fisheries and Oceans, Open Canada, Open Canada [data set], Record ID: e6e11b99-f0cc-44f7-f5eb-3b995fb1637e, 2021.
NRCan: The Expedition Database (ED), Natural Resources Canada – Grain Size Data,
https://ed.marine-geo.canada.ca/index_e.php (last access: 21 November 2022), 2022.
Pace, M. C., Bailey, D. M., Donnan, D. W., Narayanaswamy, B. E., Smith, H. J., Speirs, D. C., Turrell, W. R., and Heath, M. R.: Modelling seabed sediment physical properties and organic matter content in the Firth of Clyde, Earth Syst. Sci. Data, 13, 5847–5866, https://doi.org/10.5194/essd-13-5847-2021, 2021.
PANGAEA®: Data Publisher for Earth & Environmental Science, PANGAEA [data set], https://doi.org/10.1594/PANGAEA, 2022.
Paradis, S., Nakajima, K., Van der Voort, T. S., Gies, H., Wildberger, A., Blattmann, T. M., Bröder, L., and Eglinton, T. I.: The Modern Ocean Sediment Archive and Inventory of Carbon (MOSAIC): version 2.0, Earth Syst. Sci. Data, 15, 4105–4125, https://doi.org/10.5194/essd-15-4105-2023, 2023.
Pebesma, E.: Simple Features for R: Standardized Support for Spatial Vector Data, R J., 10, 439–446, https://doi.org/10.32614/RJ-2018-009, 2018.
Pebesma, E. and Bivand, R.: Spatial Data Science: With applications in R. Chapman and Hall/CRC, London, https://doi.org/10.1201/9780429459016, 2023.
Pedersen, T. L.: patchwork: The Composer of Plots, R package version 1.1.2, CRAN [code],
https://cran.r-project.org/package=patchwork (last access: 24 November 2022), 2022.
Peña, M. A., Fine, I., and Callendar, W.: Interannual variability in primary production and shelf-offshore transport of nutrients along the northeast Pacific Ocean margin, Deep-Sea Res. Pt. II, 169–170, 104637, https://doi.org/10.1016/j.dsr2.2019.104637, 2019.
Philibert, G., Todd, B. J., Campbell, D. C., King, E. L., Normandeau, A., Hayward, S. E., Patton, E. R., and Campbell, L.: Updated surficial geology compilation of the Scotian Shelf bioregion, offshore Nova Scotia and New Brunswick, Geological Survey of Canada – Open file, 8911, .zip file, https://doi.org/10.4095/330474, 2022.
Posit Team: RStudio: Integrated Development Environment for R, Posit Software, PBC, Boston, MA,
https://posit.co/products/open-source/rstudio/ (last access: 24 November 2022), 2022.
Probst, P., Wright, M. N., and Boulesteix, A.-L.: Hyperparameters and tuning strategies for random forest, WIREs Data Mining and Knowledge Discovery, 9, e1301, https://doi.org/10.1002/widm.1301, 2019.
QGIS.org: QGIS Geographic Information System, QGIS Association,
http://www.qgis.org (last access: 15 May 2023), 2021.
Raven, J.: Blue carbon: past, present and future, with emphasis on macroalgae, Biol. Lett., 14, 20180336, https://doi.org/10.1098/rsbl.2018.0336, 2018.
R Core Team: R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria,
https://www.R-project.org/ (last access: 24 November 2022), 2022.
Restreppo, G. A., Wood, W. T., Graw, J. H., and Phrampus, B. J.: A machine-learning derived model of seafloor sediment accumulation, Mar. Geol., 440, 106577, https://doi.org/10.1016/j.margeo.2021.106577, 2021.
Roy, M.-H. and Larocque, D.: Prediction intervals with random forests, Stat Methods Med. Res., 29, 205–229, https://doi.org/10.1177/0962280219829885, 2020.
Seiter, K., Hensen, C., Schröter, J., and Zabel, M.: Organic carbon content in surface sediments–defining regional provinces, Deep-Sea Res. Pt. I, 51, 2001–2026, https://doi.org/10.1016/j.dsr.2004.06.014, 2004.
Smeaton, C., Hunt, C. A., Turrell, W. R., and Austin, W. E. N.: Marine Sedimentary Carbon Stocks of the United Kingdom's Exclusive Economic Zone, Front. Earth Sci., 9, 50, https://doi.org/10.3389/feart.2021.593324, 2021.
Snelgrove, P. V. R., Soetaert, K., Solan, M., Thrush, S., Wei, C. L., Danovaro, R., Fulweiler, R. W., Kitazato, H., Ingole, B., Norkko, A., Parkes, R. J., and Volkenborn, N.: Global Carbon Cycling on a Heterogeneous Seafloor, Trends Ecol. Evol., 33, 96–105, https://doi.org/10.1016/j.tree.2017.11.004, 2018.
Soontiens, N. and Allen, S. E.: Modelling sensitivities to mixing and advection in a sill-basin estuarine system, Ocean Model., 112, 17–32, https://doi.org/10.1016/j.ocemod.2017.02.008, 2017.
Soontiens, N., Allen, S. E., Latornell, D., Le Souëf, K., Machuca, I., Paquin, J.-P., Lu, Y., Thompson, K., and Korabel, V.: Storm Surges in the Strait of Georgia Simulated with a Regional Model, Atmos.-Ocean, 54, 1–21, https://doi.org/10.1080/07055900.2015.1108899, 2016.
Sothe, C., Gonsamo, A., Arabian, J., Kurz, W. A., Finkelstein, S. A., and Snider, J.: Large Soil Carbon Storage in Terrestrial Ecosystems of Canada, Global Biogeochem. Cy., 36, e2021GB007213, https://doi.org/10.1029/2021GB007213, 2022.
Soulsby, R. L.: Simplified calculation of wave orbital velocities, Report TR 155 – HR Wallingford, 1,
http://eprints.hrwallingford.com/id/eprint/588 (last access: 10 January 2023), 2006.
Stephens, D. and Diesing, M.: Towards Quantitative Spatial Models of Seabed Sediment Composition, PLOS ONE, 10, e0142502, https://doi.org/10.1371/journal.pone.0142502, 2015.
Sumner, M.: tidync: A Tidy Approach to “NetCDF” Data Exploration and Extraction, R package version 0.3.0, CRAN [code],
https://cran.r-project.org/web/packages/tidync/index.html (last access: 24 November 2022), 2022.
Turner, J. T.: Zooplankton fecal pellets, marine snow, phytodetritus and the ocean's biological pump, Prog. Oceanogr., 130, 205–248, https://doi.org/10.1016/j.pocean.2014.08.005, 2015.
VERRA: Methods for Monitoring of Carbon Stock Changes and Greenhouse Gas Emissions and Removals in Tidal Wetland Restoration and Conservation Project Activities (M-TW), VCS Module VMD0051, Sectoiral Scope 14, 1, https://verra.org/methodologies/methods-for-monitoring-of-carbon-stock-changes-and-greenhouse-gas-emissions-and-removals-in-tidal-wetland-restoration-and-conservation-project-activities-m-tw-v1-0/ (last access: 18 March 2023), 2020.
Wickham, H., François, R., Henry, L., Müller, K., and Vaughan, D.: Welcome to the
{tidyverse}, J. Open Source Softw., 4, 1686, https://doi.org/10.21105/joss.01686, 2019.
Wilson, R. J., Speirs, D. C., Sabatino, A., and Heath, M. R.: A synthetic map of the north-west European Shelf sedimentary environment for applications in marine science, Earth Syst. Sci. Data, 10, 109–130, https://doi.org/10.5194/essd-10-109-2018, 2018.
Wood, S. N., Pya, N., and Säfken, B.: Smoothing Parameter and Model Selection for General Smooth Models, J. Am. Stat. Assoc., 111, 1548–1563, https://doi.org/10.1080/01621459.2016.1180986, 2016.
Wright, M. N. and Ziegler, A.:
{ranger
}: A Fast Implementation of Random Forests for High Dimensional Data in
{C
} and
{R
}, J. Stat. Softw., 77, 1–17, 2017.
Zhang, X., Chen, S., Xue, J., Wang, N., Xiao, Y., Chen, Q., Hong, Y., Zhou, Y., Teng, H., Hu, B., Zhuo, Z., Ji, W., Huang, Y., Gou, Y., Richer-de-Forges, A. C., Arrouays, D., and Shi, Z.: Improving model parsimony and accuracy by modified greedy feature selection in digital soil mapping, Geoderma, 432, 116383, https://doi.org/10.1016/j.geoderma.2023.116383, 2023.
