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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...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2022
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| Materias: | |
| 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 |