June 10, 2024, 4:45 a.m. | Matthew Fortier, Mats L. Richter, Oliver Sonnentag, Chris Pal

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.04940v1 Announce Type: new
Abstract: Terrestrial carbon fluxes provide vital information about our biosphere's health and its capacity to absorb anthropogenic CO$_2$ emissions. The importance of predicting carbon fluxes has led to the emerging field of data-driven carbon flux modelling (DDCFM), which uses statistical techniques to predict carbon fluxes from biophysical data. However, the field lacks a standardized dataset to promote comparisons between models. To address this gap, we present CarbonSense, the first machine learning-ready dataset for DDCFM. CarbonSense integrates …

abstract arxiv capacity carbon cs.ai cs.lg data data-driven dataset emissions health importance information modelling multimodal statistical type vital

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