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identifying climate-resilient restoration sites in the Baltic Sea. The project integrates regional climate modeling, AI-driven simulations, and biogeochemical modeling to predict seagrass distribution under
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, aggregation, linking and retrieval of comprehensive heterogeneous and distributed data sources. To this end, both statistical and linguistic analysis methods (NLP) as well as machine learning in combination
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