Benchmark performance of state-of- the-art prediction systems (D4.2)
The Blue-Action WP4 research within Task 4.1 has been mainly focusing on assessing in a mechanistic way the key processes responsible for the Arctic-lower latitude predictive linkages and their representation in the current state-of-art prediction systems. The Blue-Action project has received fundin...
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ftzenodo:oai:zenodo.org:3574097 2023-05-15T14:55:25+02:00 Benchmark performance of state-of- the-art prediction systems (D4.2) Matei, Daniela 2019-12-13 https://zenodo.org/record/3574097 https://doi.org/10.5281/zenodo.3574097 eng eng info:eu-repo/grantAgreement/EC/H2020/727852/ doi:10.5281/zenodo.3574096 https://zenodo.org/communities/blue-actionh2020 https://zenodo.org/record/3574097 https://doi.org/10.5281/zenodo.3574097 oai:zenodo.org:3574097 info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode info:eu-repo/semantics/report publication-deliverable 2019 ftzenodo https://doi.org/10.5281/zenodo.357409710.5281/zenodo.3574096 2023-03-11T00:31:12Z The Blue-Action WP4 research within Task 4.1 has been mainly focusing on assessing in a mechanistic way the key processes responsible for the Arctic-lower latitude predictive linkages and their representation in the current state-of-art prediction systems. The Blue-Action project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 727852. Report Arctic Zenodo Arctic |
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English |
description |
The Blue-Action WP4 research within Task 4.1 has been mainly focusing on assessing in a mechanistic way the key processes responsible for the Arctic-lower latitude predictive linkages and their representation in the current state-of-art prediction systems. The Blue-Action project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement No 727852. |
format |
Report |
author |
Matei, Daniela |
spellingShingle |
Matei, Daniela Benchmark performance of state-of- the-art prediction systems (D4.2) |
author_facet |
Matei, Daniela |
author_sort |
Matei, Daniela |
title |
Benchmark performance of state-of- the-art prediction systems (D4.2) |
title_short |
Benchmark performance of state-of- the-art prediction systems (D4.2) |
title_full |
Benchmark performance of state-of- the-art prediction systems (D4.2) |
title_fullStr |
Benchmark performance of state-of- the-art prediction systems (D4.2) |
title_full_unstemmed |
Benchmark performance of state-of- the-art prediction systems (D4.2) |
title_sort |
benchmark performance of state-of- the-art prediction systems (d4.2) |
publishDate |
2019 |
url |
https://zenodo.org/record/3574097 https://doi.org/10.5281/zenodo.3574097 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
info:eu-repo/grantAgreement/EC/H2020/727852/ doi:10.5281/zenodo.3574096 https://zenodo.org/communities/blue-actionh2020 https://zenodo.org/record/3574097 https://doi.org/10.5281/zenodo.3574097 oai:zenodo.org:3574097 |
op_rights |
info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.5281/zenodo.357409710.5281/zenodo.3574096 |
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1766327305461825536 |