Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments

Previous attempts to quantify the amount of sedimentary carbonate precipitation in modern marine sediments have been derived from the flux of calcium into the sediments due to diffusion under assumed steady state, the application of Fick's first law, and then extrapolation of these site-specific rates to the global ocean sediment column. This approach is limited, however, as much of the ocean floor has not been sampled. We take a machine learning approach to update and refine the estimate of the amount of sedimentary carbonate precipitation, as well as define whether sedimentary carbonate precipitation is driven by organoclastic microbial sulfate reduction or anaerobic methane oxidation. We identify areas where there is sedimentary carbonate formation using machine learning, based upon oceanic physical and chemical properties including bathymetry, temperature, water depth, distance from shore, and tracers of primary production, and data from the global ODP/IODP database. Our results suggest that the total amount of sedimentary carbonate formation is much lower than previous estimates, at 1.35 ± 0.5 × 10 11 molC/yr. We suggest that this rate is a lower estimate and discuss why machine-learning approaches may always produce lower-bound estimates of global processes. Our calculations suggest that the formation of sedimentary carbonate today is mainly driven by anaerobic methane oxidation (77%), with the remainder attributed to organoclastic sulfate reduction. We use our machine-learning results to speculate the impact that sedimentary carbonate precipitation may have had on the carbon isotope composition of the surface dissolved inorganic carbon reservoir over Earth history. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Bradbury, Harold J. [verfasserIn] Turchyn, Alexandra V. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	authigenic carbonate machine learning

Übergeordnetes Werk:	Enthalten in: Earth and planetary science letters - Amsterdam [u.a.] : Elsevier, 1966, 519, Seite 40-49
Übergeordnetes Werk:	volume:519 ; pages:40-49

DOI / URN:	10.1016/j.epsl.2019.04.044

Katalog-ID:	ELV002362759

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100	1		\|a Bradbury, Harold J. \|e verfasserin \|0 (orcid)0000-0002-6937-9755 \|4 aut
245	1	0	\|a Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments
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520			\|a Previous attempts to quantify the amount of sedimentary carbonate precipitation in modern marine sediments have been derived from the flux of calcium into the sediments due to diffusion under assumed steady state, the application of Fick's first law, and then extrapolation of these site-specific rates to the global ocean sediment column. This approach is limited, however, as much of the ocean floor has not been sampled. We take a machine learning approach to update and refine the estimate of the amount of sedimentary carbonate precipitation, as well as define whether sedimentary carbonate precipitation is driven by organoclastic microbial sulfate reduction or anaerobic methane oxidation. We identify areas where there is sedimentary carbonate formation using machine learning, based upon oceanic physical and chemical properties including bathymetry, temperature, water depth, distance from shore, and tracers of primary production, and data from the global ODP/IODP database. Our results suggest that the total amount of sedimentary carbonate formation is much lower than previous estimates, at 1.35 ± 0.5 × 10 11 molC/yr. We suggest that this rate is a lower estimate and discuss why machine-learning approaches may always produce lower-bound estimates of global processes. Our calculations suggest that the formation of sedimentary carbonate today is mainly driven by anaerobic methane oxidation (77%), with the remainder attributed to organoclastic sulfate reduction. We use our machine-learning results to speculate the impact that sedimentary carbonate precipitation may have had on the carbon isotope composition of the surface dissolved inorganic carbon reservoir over Earth history.
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matchkey_str	article:1385013X:2019----::evlaighcrosndeoeietrcroaeomtoi
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bklnumber	38.35 39.29
publishDate	2019
allfields	10.1016/j.epsl.2019.04.044 doi (DE-627)ELV002362759 (ELSEVIER)S0012-821X(19)30248-1 DE-627 ger DE-627 rda eng 550 VZ 38.35 bkl 39.29 bkl Bradbury, Harold J. verfasserin (orcid)0000-0002-6937-9755 aut Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Previous attempts to quantify the amount of sedimentary carbonate precipitation in modern marine sediments have been derived from the flux of calcium into the sediments due to diffusion under assumed steady state, the application of Fick's first law, and then extrapolation of these site-specific rates to the global ocean sediment column. This approach is limited, however, as much of the ocean floor has not been sampled. We take a machine learning approach to update and refine the estimate of the amount of sedimentary carbonate precipitation, as well as define whether sedimentary carbonate precipitation is driven by organoclastic microbial sulfate reduction or anaerobic methane oxidation. We identify areas where there is sedimentary carbonate formation using machine learning, based upon oceanic physical and chemical properties including bathymetry, temperature, water depth, distance from shore, and tracers of primary production, and data from the global ODP/IODP database. Our results suggest that the total amount of sedimentary carbonate formation is much lower than previous estimates, at 1.35 ± 0.5 × 10 11 molC/yr. We suggest that this rate is a lower estimate and discuss why machine-learning approaches may always produce lower-bound estimates of global processes. Our calculations suggest that the formation of sedimentary carbonate today is mainly driven by anaerobic methane oxidation (77%), with the remainder attributed to organoclastic sulfate reduction. We use our machine-learning results to speculate the impact that sedimentary carbonate precipitation may have had on the carbon isotope composition of the surface dissolved inorganic carbon reservoir over Earth history. authigenic carbonate machine learning Turchyn, Alexandra V. verfasserin (orcid)0000-0002-9298-2173 aut Enthalten in Earth and planetary science letters Amsterdam [u.a.] : Elsevier, 1966 519, Seite 40-49 Online-Ressource (DE-627)266015778 (DE-600)1466659-5 (DE-576)074959980 1385-013X nnns volume:519 pages:40-49 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.35 Endogene Geologie: Allgemeines VZ 39.29 Theoretische Astronomie: Sonstiges VZ AR 519 40-49
spelling	10.1016/j.epsl.2019.04.044 doi (DE-627)ELV002362759 (ELSEVIER)S0012-821X(19)30248-1 DE-627 ger DE-627 rda eng 550 VZ 38.35 bkl 39.29 bkl Bradbury, Harold J. verfasserin (orcid)0000-0002-6937-9755 aut Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Previous attempts to quantify the amount of sedimentary carbonate precipitation in modern marine sediments have been derived from the flux of calcium into the sediments due to diffusion under assumed steady state, the application of Fick's first law, and then extrapolation of these site-specific rates to the global ocean sediment column. This approach is limited, however, as much of the ocean floor has not been sampled. We take a machine learning approach to update and refine the estimate of the amount of sedimentary carbonate precipitation, as well as define whether sedimentary carbonate precipitation is driven by organoclastic microbial sulfate reduction or anaerobic methane oxidation. We identify areas where there is sedimentary carbonate formation using machine learning, based upon oceanic physical and chemical properties including bathymetry, temperature, water depth, distance from shore, and tracers of primary production, and data from the global ODP/IODP database. Our results suggest that the total amount of sedimentary carbonate formation is much lower than previous estimates, at 1.35 ± 0.5 × 10 11 molC/yr. We suggest that this rate is a lower estimate and discuss why machine-learning approaches may always produce lower-bound estimates of global processes. Our calculations suggest that the formation of sedimentary carbonate today is mainly driven by anaerobic methane oxidation (77%), with the remainder attributed to organoclastic sulfate reduction. We use our machine-learning results to speculate the impact that sedimentary carbonate precipitation may have had on the carbon isotope composition of the surface dissolved inorganic carbon reservoir over Earth history. authigenic carbonate machine learning Turchyn, Alexandra V. verfasserin (orcid)0000-0002-9298-2173 aut Enthalten in Earth and planetary science letters Amsterdam [u.a.] : Elsevier, 1966 519, Seite 40-49 Online-Ressource (DE-627)266015778 (DE-600)1466659-5 (DE-576)074959980 1385-013X nnns volume:519 pages:40-49 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.35 Endogene Geologie: Allgemeines VZ 39.29 Theoretische Astronomie: Sonstiges VZ AR 519 40-49
allfields_unstemmed	10.1016/j.epsl.2019.04.044 doi (DE-627)ELV002362759 (ELSEVIER)S0012-821X(19)30248-1 DE-627 ger DE-627 rda eng 550 VZ 38.35 bkl 39.29 bkl Bradbury, Harold J. verfasserin (orcid)0000-0002-6937-9755 aut Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Previous attempts to quantify the amount of sedimentary carbonate precipitation in modern marine sediments have been derived from the flux of calcium into the sediments due to diffusion under assumed steady state, the application of Fick's first law, and then extrapolation of these site-specific rates to the global ocean sediment column. This approach is limited, however, as much of the ocean floor has not been sampled. We take a machine learning approach to update and refine the estimate of the amount of sedimentary carbonate precipitation, as well as define whether sedimentary carbonate precipitation is driven by organoclastic microbial sulfate reduction or anaerobic methane oxidation. We identify areas where there is sedimentary carbonate formation using machine learning, based upon oceanic physical and chemical properties including bathymetry, temperature, water depth, distance from shore, and tracers of primary production, and data from the global ODP/IODP database. Our results suggest that the total amount of sedimentary carbonate formation is much lower than previous estimates, at 1.35 ± 0.5 × 10 11 molC/yr. We suggest that this rate is a lower estimate and discuss why machine-learning approaches may always produce lower-bound estimates of global processes. Our calculations suggest that the formation of sedimentary carbonate today is mainly driven by anaerobic methane oxidation (77%), with the remainder attributed to organoclastic sulfate reduction. We use our machine-learning results to speculate the impact that sedimentary carbonate precipitation may have had on the carbon isotope composition of the surface dissolved inorganic carbon reservoir over Earth history. authigenic carbonate machine learning Turchyn, Alexandra V. verfasserin (orcid)0000-0002-9298-2173 aut Enthalten in Earth and planetary science letters Amsterdam [u.a.] : Elsevier, 1966 519, Seite 40-49 Online-Ressource (DE-627)266015778 (DE-600)1466659-5 (DE-576)074959980 1385-013X nnns volume:519 pages:40-49 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.35 Endogene Geologie: Allgemeines VZ 39.29 Theoretische Astronomie: Sonstiges VZ AR 519 40-49
allfieldsGer	10.1016/j.epsl.2019.04.044 doi (DE-627)ELV002362759 (ELSEVIER)S0012-821X(19)30248-1 DE-627 ger DE-627 rda eng 550 VZ 38.35 bkl 39.29 bkl Bradbury, Harold J. verfasserin (orcid)0000-0002-6937-9755 aut Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Previous attempts to quantify the amount of sedimentary carbonate precipitation in modern marine sediments have been derived from the flux of calcium into the sediments due to diffusion under assumed steady state, the application of Fick's first law, and then extrapolation of these site-specific rates to the global ocean sediment column. This approach is limited, however, as much of the ocean floor has not been sampled. We take a machine learning approach to update and refine the estimate of the amount of sedimentary carbonate precipitation, as well as define whether sedimentary carbonate precipitation is driven by organoclastic microbial sulfate reduction or anaerobic methane oxidation. We identify areas where there is sedimentary carbonate formation using machine learning, based upon oceanic physical and chemical properties including bathymetry, temperature, water depth, distance from shore, and tracers of primary production, and data from the global ODP/IODP database. Our results suggest that the total amount of sedimentary carbonate formation is much lower than previous estimates, at 1.35 ± 0.5 × 10 11 molC/yr. We suggest that this rate is a lower estimate and discuss why machine-learning approaches may always produce lower-bound estimates of global processes. Our calculations suggest that the formation of sedimentary carbonate today is mainly driven by anaerobic methane oxidation (77%), with the remainder attributed to organoclastic sulfate reduction. We use our machine-learning results to speculate the impact that sedimentary carbonate precipitation may have had on the carbon isotope composition of the surface dissolved inorganic carbon reservoir over Earth history. authigenic carbonate machine learning Turchyn, Alexandra V. verfasserin (orcid)0000-0002-9298-2173 aut Enthalten in Earth and planetary science letters Amsterdam [u.a.] : Elsevier, 1966 519, Seite 40-49 Online-Ressource (DE-627)266015778 (DE-600)1466659-5 (DE-576)074959980 1385-013X nnns volume:519 pages:40-49 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.35 Endogene Geologie: Allgemeines VZ 39.29 Theoretische Astronomie: Sonstiges VZ AR 519 40-49
allfieldsSound	10.1016/j.epsl.2019.04.044 doi (DE-627)ELV002362759 (ELSEVIER)S0012-821X(19)30248-1 DE-627 ger DE-627 rda eng 550 VZ 38.35 bkl 39.29 bkl Bradbury, Harold J. verfasserin (orcid)0000-0002-6937-9755 aut Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Previous attempts to quantify the amount of sedimentary carbonate precipitation in modern marine sediments have been derived from the flux of calcium into the sediments due to diffusion under assumed steady state, the application of Fick's first law, and then extrapolation of these site-specific rates to the global ocean sediment column. This approach is limited, however, as much of the ocean floor has not been sampled. We take a machine learning approach to update and refine the estimate of the amount of sedimentary carbonate precipitation, as well as define whether sedimentary carbonate precipitation is driven by organoclastic microbial sulfate reduction or anaerobic methane oxidation. We identify areas where there is sedimentary carbonate formation using machine learning, based upon oceanic physical and chemical properties including bathymetry, temperature, water depth, distance from shore, and tracers of primary production, and data from the global ODP/IODP database. Our results suggest that the total amount of sedimentary carbonate formation is much lower than previous estimates, at 1.35 ± 0.5 × 10 11 molC/yr. We suggest that this rate is a lower estimate and discuss why machine-learning approaches may always produce lower-bound estimates of global processes. Our calculations suggest that the formation of sedimentary carbonate today is mainly driven by anaerobic methane oxidation (77%), with the remainder attributed to organoclastic sulfate reduction. We use our machine-learning results to speculate the impact that sedimentary carbonate precipitation may have had on the carbon isotope composition of the surface dissolved inorganic carbon reservoir over Earth history. authigenic carbonate machine learning Turchyn, Alexandra V. verfasserin (orcid)0000-0002-9298-2173 aut Enthalten in Earth and planetary science letters Amsterdam [u.a.] : Elsevier, 1966 519, Seite 40-49 Online-Ressource (DE-627)266015778 (DE-600)1466659-5 (DE-576)074959980 1385-013X nnns volume:519 pages:40-49 GBV_USEFLAG_U SYSFLAG_U GBV_ELV SSG-OPC-GGO SSG-OPC-GEO SSG-OPC-AST GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 38.35 Endogene Geologie: Allgemeines VZ 39.29 Theoretische Astronomie: Sonstiges VZ AR 519 40-49
language	English
source	Enthalten in Earth and planetary science letters 519, Seite 40-49 volume:519 pages:40-49
sourceStr	Enthalten in Earth and planetary science letters 519, Seite 40-49 volume:519 pages:40-49
format_phy_str_mv	Article
bklname	Endogene Geologie: Allgemeines Theoretische Astronomie: Sonstiges
institution	findex.gbv.de
topic_facet	authigenic carbonate machine learning
dewey-raw	550
isfreeaccess_bool	false
container_title	Earth and planetary science letters
authorswithroles_txt_mv	Bradbury, Harold J. @@aut@@ Turchyn, Alexandra V. @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	266015778
dewey-sort	3550
id	ELV002362759
language_de	englisch
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author	Bradbury, Harold J.
spellingShingle	Bradbury, Harold J. ddc 550 bkl 38.35 bkl 39.29 misc authigenic carbonate misc machine learning Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments
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topic_title	550 VZ 38.35 bkl 39.29 bkl Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments authigenic carbonate machine learning
topic	ddc 550 bkl 38.35 bkl 39.29 misc authigenic carbonate misc machine learning
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title	Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments
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title_full	Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments
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title_sort	reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments
title_auth	Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments
abstract	Previous attempts to quantify the amount of sedimentary carbonate precipitation in modern marine sediments have been derived from the flux of calcium into the sediments due to diffusion under assumed steady state, the application of Fick's first law, and then extrapolation of these site-specific rates to the global ocean sediment column. This approach is limited, however, as much of the ocean floor has not been sampled. We take a machine learning approach to update and refine the estimate of the amount of sedimentary carbonate precipitation, as well as define whether sedimentary carbonate precipitation is driven by organoclastic microbial sulfate reduction or anaerobic methane oxidation. We identify areas where there is sedimentary carbonate formation using machine learning, based upon oceanic physical and chemical properties including bathymetry, temperature, water depth, distance from shore, and tracers of primary production, and data from the global ODP/IODP database. Our results suggest that the total amount of sedimentary carbonate formation is much lower than previous estimates, at 1.35 ± 0.5 × 10 11 molC/yr. We suggest that this rate is a lower estimate and discuss why machine-learning approaches may always produce lower-bound estimates of global processes. Our calculations suggest that the formation of sedimentary carbonate today is mainly driven by anaerobic methane oxidation (77%), with the remainder attributed to organoclastic sulfate reduction. We use our machine-learning results to speculate the impact that sedimentary carbonate precipitation may have had on the carbon isotope composition of the surface dissolved inorganic carbon reservoir over Earth history.
abstractGer	Previous attempts to quantify the amount of sedimentary carbonate precipitation in modern marine sediments have been derived from the flux of calcium into the sediments due to diffusion under assumed steady state, the application of Fick's first law, and then extrapolation of these site-specific rates to the global ocean sediment column. This approach is limited, however, as much of the ocean floor has not been sampled. We take a machine learning approach to update and refine the estimate of the amount of sedimentary carbonate precipitation, as well as define whether sedimentary carbonate precipitation is driven by organoclastic microbial sulfate reduction or anaerobic methane oxidation. We identify areas where there is sedimentary carbonate formation using machine learning, based upon oceanic physical and chemical properties including bathymetry, temperature, water depth, distance from shore, and tracers of primary production, and data from the global ODP/IODP database. Our results suggest that the total amount of sedimentary carbonate formation is much lower than previous estimates, at 1.35 ± 0.5 × 10 11 molC/yr. We suggest that this rate is a lower estimate and discuss why machine-learning approaches may always produce lower-bound estimates of global processes. Our calculations suggest that the formation of sedimentary carbonate today is mainly driven by anaerobic methane oxidation (77%), with the remainder attributed to organoclastic sulfate reduction. We use our machine-learning results to speculate the impact that sedimentary carbonate precipitation may have had on the carbon isotope composition of the surface dissolved inorganic carbon reservoir over Earth history.
abstract_unstemmed	Previous attempts to quantify the amount of sedimentary carbonate precipitation in modern marine sediments have been derived from the flux of calcium into the sediments due to diffusion under assumed steady state, the application of Fick's first law, and then extrapolation of these site-specific rates to the global ocean sediment column. This approach is limited, however, as much of the ocean floor has not been sampled. We take a machine learning approach to update and refine the estimate of the amount of sedimentary carbonate precipitation, as well as define whether sedimentary carbonate precipitation is driven by organoclastic microbial sulfate reduction or anaerobic methane oxidation. We identify areas where there is sedimentary carbonate formation using machine learning, based upon oceanic physical and chemical properties including bathymetry, temperature, water depth, distance from shore, and tracers of primary production, and data from the global ODP/IODP database. Our results suggest that the total amount of sedimentary carbonate formation is much lower than previous estimates, at 1.35 ± 0.5 × 10 11 molC/yr. We suggest that this rate is a lower estimate and discuss why machine-learning approaches may always produce lower-bound estimates of global processes. Our calculations suggest that the formation of sedimentary carbonate today is mainly driven by anaerobic methane oxidation (77%), with the remainder attributed to organoclastic sulfate reduction. We use our machine-learning results to speculate the impact that sedimentary carbonate precipitation may have had on the carbon isotope composition of the surface dissolved inorganic carbon reservoir over Earth history.
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title_short	Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments
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up_date	2024-07-07T00:28:50.837Z
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Reevaluating the carbon sink due to sedimentary carbonate formation in modern marine sediments

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