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 fund...
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ftdatacite:10.5281/zenodo.3574097 2023-05-15T14:55:30+02:00 Benchmark performance of state-of- the-art prediction systems (D4.2) Matei, Daniela 2019 https://dx.doi.org/10.5281/zenodo.3574097 https://zenodo.org/record/3574097 en eng Zenodo https://zenodo.org/communities/blue-actionh2020 https://dx.doi.org/10.5281/zenodo.3574096 https://zenodo.org/communities/blue-actionh2020 Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY Text Project deliverable article-journal ScholarlyArticle 2019 ftdatacite https://doi.org/10.5281/zenodo.3574097 https://doi.org/10.5281/zenodo.3574096 2021-11-05T12:55:41Z 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. Text Arctic DataCite Metadata Store (German National Library of Science and Technology) Arctic |
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DataCite Metadata Store (German National Library of Science and Technology) |
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ftdatacite |
language |
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 |
Text |
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) |
publisher |
Zenodo |
publishDate |
2019 |
url |
https://dx.doi.org/10.5281/zenodo.3574097 https://zenodo.org/record/3574097 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_relation |
https://zenodo.org/communities/blue-actionh2020 https://dx.doi.org/10.5281/zenodo.3574096 https://zenodo.org/communities/blue-actionh2020 |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.3574097 https://doi.org/10.5281/zenodo.3574096 |
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1766327450601521152 |