Articles | Volume 16, issue 6
https://doi.org/10.5194/cp-16-2599-2020
https://doi.org/10.5194/cp-16-2599-2020
Research article
 | 
23 Dec 2020
Research article |  | 23 Dec 2020

OPTiMAL: a new machine learning approach for GDGT-based palaeothermometry

Tom Dunkley Jones, Yvette L. Eley, William Thomson, Sarah E. Greene, Ilya Mandel, Kirsty Edgar, and James A. Bendle

Viewed

Total article views: 5,159 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
3,668 1,371 120 5,159 898 113 130
  • HTML: 3,668
  • PDF: 1,371
  • XML: 120
  • Total: 5,159
  • Supplement: 898
  • BibTeX: 113
  • EndNote: 130
Views and downloads (calculated since 06 Jun 2019)
Cumulative views and downloads (calculated since 06 Jun 2019)

Viewed (geographical distribution)

Total article views: 5,159 (including HTML, PDF, and XML) Thereof 4,582 with geography defined and 577 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2024
Download
Short summary
We explore the utiliity of the composition of fossil lipid biomarkers, which are commonly preserved in ancient marine sediments, in providing estimates of past ocean temperatures. The group of lipids concerned show compositional changes across the modern oceans that are correlated, to some extent, with local surface ocean temperatures. Here we present new machine learning approaches to improve our understanding of this temperature sensitivity and its application to reconstructing past climates.