Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands

37 earth sciences (for-2020), 30 agricultural, veterinary and food sciences (for-2020), 31 biological sciences (for-2020), machine learning and artificial intelligence (rcdc), networking and information technology r&d (nitrd) (rcdc), bioengineering (rcdc), machine learning