Articles | Volume 18, issue 9
https://doi.org/10.5194/gmd-18-2521-2025
https://doi.org/10.5194/gmd-18-2521-2025
Development and technical paper
 | 
08 May 2025
Development and technical paper |  | 08 May 2025

NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks

Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on egusphere-2024-1360', Juan Antonio Añel, 20 Jun 2024
    • AC1: 'Reply on CEC1', Naveenkumar Parameswaran, 21 Jun 2024
      • CEC2: 'Reply on AC1', Juan Antonio Añel, 21 Jun 2024
  • RC1: 'Comment on egusphere-2024-1360', Taylor Lee, 24 Jun 2024
    • RC2: 'Reply on RC1', Taylor Lee, 24 Jun 2024
    • AC2: 'Reply on RC1', Naveenkumar Parameswaran, 25 Jul 2024
  • RC3: 'Comment on egusphere-2024-1360', Sarah Paradis, 30 Jul 2024
    • AC3: 'Reply on RC3', Naveenkumar Parameswaran, 25 Oct 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Naveenkumar Parameswaran on behalf of the Authors (13 Nov 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (19 Dec 2024) by Sandra Arndt
RR by Taylor Lee (02 Jan 2025)
ED: Publish as is (11 Feb 2025) by Sandra Arndt
AR by Naveenkumar Parameswaran on behalf of the Authors (20 Feb 2025)  Manuscript 

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Naveenkumar Parameswaran on behalf of the Authors (25 Apr 2025)   Author's adjustment   Manuscript
EA: Adjustments approved (05 May 2025) by Sandra Arndt
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Short summary
Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
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