{"refrec":{"BRefID":358963,"RR":"<b>Lee, T.R.; Wood, W. T.; Phrampus, B.J.</b> (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. <i>Global Biogeochem. Cycles 33(1)</i>: 37-46. <a href=\"https://dx.doi.org/10.1029/2018gb005992\" target=\"_blank\">https://dx.doi.org/10.1029/2018gb005992</a>","BEntID":356678,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Global Biogeochem. Cycles 33(1)</i>: 37-46. <a href=\"https://dx.doi.org/10.1029/2018gb005992\" target=\"_blank\">https://dx.doi.org/10.1029/2018gb005992</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Lee, T.R.; Wood, W. T.; Phrampus, B.J.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Lee, T.R.; Wood, W. T.; Phrampus, B.J.","Englishabstract":"Seafloor properties, including total organic carbon (TOC), are sparsely measured on a global scale, and interpolation (prediction) techniques are often used as a proxy for observation. Previous geospatial interpolations of seafloor TOC exhibit gaps where little to no observed data exists. In contrast, recent machine learning techniques, relying on geophysical and geochemical properties (e.g., seafloor biomass, porosity, and distance from coast), show promise in making comprehensive, statistically optimal predictions. Here we apply a nonparametric (i.e., data-driven) machine learning algorithm, specifically k-nearest neighbors (kNN), to estimate the global distribution of seafloor TOC. Our results include predictor (feature) selection specifically designed to mitigate bias and produce a statistically optimal estimation of seafloor TOC, with uncertainty, at 5 × 5-arc minute resolution. Analysis of parameter space sample density provides a guide for future sampling. One use for this prediction is to constrain a global inventory, indicating that just the upper 5 cm of the seafloor contains about 87 ± 43 gigatons of carbon (Gt C) in organic form.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"A machine learning (kNN) approach to predicting global seafloor total organic carbon","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-04-24 01:33:16.474536","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2019,"MonPub":null,"DateUpdate":"2022-10-31","DateCreate":"2022-10-31","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000458243500003","VABBcode":null,"OpenAcc":1,"DOI":"10.1029/2018gb005992"},"refs":null,"anarec":{"AnaID":358963,"PubliDate":2019,"Pagination":"37-46","XtraPublOfAnaID":null,"ISBN":null,"Volume":"33","Issue":"1","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":45841,"SerRR":"Global Biogeochemical Cycles. 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