Wegmann, Martin; Jaume-Santero, Fernando (2023). Artificial intelligence achieves easy-to-adapt nonlinear global temperature reconstructions using minimal local data. Communications earth & environment, 4(1) Springer Nature 10.1038/s43247-023-00872-9
|
Text
s43247-023-00872-9.pdf - Published Version Available under License Creative Commons: Attribution (CC-BY). Download (46MB) | Preview |
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
08 Faculty of Science > Institute of Geography > Physical Geography > Unit Climatology 10 Strategic Research Centers > Oeschger Centre for Climate Change Research (OCCR) 08 Faculty of Science > Institute of Geography 08 Faculty of Science > Institute of Geography > Physical Geography |
UniBE Contributor: |
Wegmann, Martin |
Subjects: |
900 History > 910 Geography & travel 500 Science > 550 Earth sciences & geology |
ISSN: |
2662-4435 |
Publisher: |
Springer Nature |
Language: |
English |
Submitter: |
Madina Susanna Vogt |
Date Deposited: |
22 Jun 2023 13:02 |
Last Modified: |
22 Jun 2023 13:02 |
Publisher DOI: |
10.1038/s43247-023-00872-9 |
BORIS DOI: |
10.48350/183630 |
URI: |
https://boris.unibe.ch/id/eprint/183630 |
Actions (login required)
 |
Edit item |