Nanopublications LDF server

Matches in Nanopublications for { <https://w3id.org/ro-id/911b0247-5b28-4993-894e-aff28828e643/> ?p ?o ?g. }

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• 911b0247-5b28-4993-894e-aff28828e643 description "The research object refers to the Sea ice forecasting using IceNet notebook published in the Environmental Data Science book. Modelling approach IceNet is a probabilistic, deep learning sea ice forecasting system. The model, an ensemble of U-Net networks, learns how sea ice changes from climate simulations and observational data to forecast up to 6 months of monthly-averaged sea ice concentration maps at 25 km resolution. IceNet advances the range of accurate sea ice forecasts, outperforming a state-of-the-art dynamical model in seasonal forecasts of summer sea ice, particularly for extreme sea ice events. IceNet was implemented in Python 3.7 using TensorFlow v2.2.0. Further details can be found in the Nature Communications paper Seasonal Arctic sea ice forecasting with probabilistic deep learning." assertion.
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• 911b0247-5b28-4993-894e-aff28828e643 contributor tom-andersson assertion.
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• 911b0247-5b28-4993-894e-aff28828e643 dateCreated "2022-04-03 22:37:45.977506+00:00" assertion.
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• 911b0247-5b28-4993-894e-aff28828e643 cite-as "Alejandro Coca-Castro, Tom Andersson, and Nick Barlow. "Sea ice forecasting using IceNet (Jupyter Notebook) forked from RO." ROHub. Apr 03 ,2022. https://w3id.org/ro-id/911b0247-5b28-4993-894e-aff28828e643." assertion.
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• 911b0247-5b28-4993-894e-aff28828e643 name "Sea ice forecasting using IceNet (Jupyter Notebook) published in the Environmental Data Science book - fork" assertion.
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• 911b0247-5b28-4993-894e-aff28828e643 dateModified "2025-10-18 10:52:01.433931+00:00" assertion.
• 911b0247-5b28-4993-894e-aff28828e643 datePublished "2022-04-03 22:37:45.977506+00:00" assertion.
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