Reconstructing Earth’s atmospheric oxygenation history using machine learning

Reconstructing historical atmospheric oxygen (O(2)) levels at finer temporal resolution is a top priority for exploring the evolution of life on Earth. This goal, however, is challenged by gaps in traditionally employed sediment-hosted geochemical proxy data. Here, we propose an independent strategy...

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Detalles Bibliográficos
Autores principales: Chen, Guoxiong, Cheng, Qiuming, Lyons, Timothy W., Shen, Jun, Agterberg, Frits, Huang, Ning, Zhao, Molei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532422/
https://www.ncbi.nlm.nih.gov/pubmed/36195593
http://dx.doi.org/10.1038/s41467-022-33388-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9532422/
https://www.ncbi.nlm.nih.gov/pubmed/36195593
http://dx.doi.org/10.1038/s41467-022-33388-5

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