2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.CE2026
Deep-learning surrogate crop modelling for scalable seasonal-to-climate crop-risk assessment
Odysseas Vlachopoulos, Andrej Ceglar, Juerg Luterbacher +3
Anticipating climate-related crop stress requires crop-risk information that is spatially explicit, probabilistic and fast enough for large seasonal forecast and climate-scenario e…
cs.CL2024★ 2 cited
Automated Fact-Checking of Climate Change Claims with Large Language Models
Markus Leippold, Saeid Ashraf Vaghefi, Dominik Stammbach +10
This paper presents Climinator, a novel AI-based tool designed to automate the fact-checking of climate change claims. Utilizing an array of Large Language Models (LLMs) informed b…