Machine learning-based identification of key biotic and abiotic drivers of mineral weathering rate in a complex enhanced weathering experiment [version 2; peer review: 1 approved, 2 approved with reservations]

Background The optimization of enhanced mineral weathering as a carbon dioxide removal technology requires a comprehensive understanding of what drives mineral weathering. These drivers can be abiotic and biotic and can interact with each other. Therefore, in this study, an extensive 8-week column e...

Full description

Saved in:

Bibliographic Details
Main Authors:	Thomas Corbett, Harun Niron, Lukas Rieder, Reinaldy P. Poetra, Abhijeet Singh, Michiel Van Tendeloo, Siegfried E. Vlaminck, Steven Latré, Jan Willem van Groenigen, Jens Hartmann, Alix Vidal, Anna Neubeck, Mathilde Hagens, Ivan A. Janssens, Tim Verdonck, Sara Vicca, Thomas Servotte, Iris Janssens, Steven Mortier, Tullia Calogiuri
Format:	Article
Language:	English
Published:	F1000 Research Ltd 2025-07-01
Series:	Open Research Europe
Subjects:	enhanced mineral weathering carbon sequestration machine learning explainable AI eng
Online Access:	https://open-research-europe.ec.europa.eu/articles/5-71/v2
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!

Machine learning-based identification of key biotic and abiotic drivers of mineral weathering rate in a complex enhanced weathering experiment [version 2; peer review: 1 approved, 2 approved with reservations]

Similar Items