The next generation of machine learning for tracking adaptation texts

Sietsma, A.J., Ford, J.D. orcid.org/0000-0002-2066-3456 and Minx, J.C. (2023) The next generation of machine learning for tracking adaptation texts. Nature Climate Change, 14. pp. 31-39. ISSN 1758-678X

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Item Type: Article
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© Springer Nature Limited 2023. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1038/s41558-023-01890-3

Dates:
  • Published: 27 December 2023
  • Published (online): 27 December 2023
  • Accepted: 13 November 2023
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Sustainability Research Institute (SRI) (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 31 Jan 2024 09:49
Last Modified: 27 Jun 2024 00:13
Status: Published
Publisher: Nature Research
Identification Number: 10.1038/s41558-023-01890-3
Open Archives Initiative ID (OAI ID):

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