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331 to 340 of 13,178 Results
T9_Biovolume_ai0p4_downProfiles.txt
Dec 8, 2021 - Stressor 2019: Zooplankton abundance, biovolume and size structure in the northern Norwegian Sea
Plain Text - 4.8 MB - MD5: af3f3021e2be18b0fcb86631bfaef30f
T9_Biovolume_downProfiles.txt
Dec 8, 2021 - Stressor 2019: Zooplankton abundance, biovolume and size structure in the northern Norwegian Sea
Plain Text - 4.8 MB - MD5: d5f816ebb88a7adecc6fc2c6481562d5
Stressor 2019: Zooplankton abundance, biovolume and size structure in the northern Norwegian Sea
Dec 8, 2021 - UiT The Arctic University of Norway
Basedow, Sünnje Linnéa, 2021, "Stressor 2019: Zooplankton abundance, biovolume and size structure in the northern Norwegian Sea", https://doi.org/10.18710/DXA0F3, DataverseNO, V1
Data on zooplankton abundance and biovolume are presented, in concert with data on the biophysical environment, from 17 transects (ca. 50-100 km long) crossing the shelf break off the Lofoten-Vesterålen islands during a research cruise with R/V Helmer Hanssen 27 April to 12 May 2019. The data were collected along vertical profiles from surface to 5...
Stressor 2019: Cruise Report
Stressor 2019: Cruise Report
Dec 8, 2021 - UiT The Arctic University of Norway
Basedow, Sünnje Linnéa, 2021, "Stressor 2019: Cruise Report", https://doi.org/10.18710/KVPUTW, DataverseNO, V1
Cruise report of the scientific cruise April/May 2019 on board R/V Helmer Hanssen as part of the Stressor project. Contains information about the type of data collected (ocean currents, physical environment, water chemistry, remote sensing, zooplankton abundance and taxonomy, lipid chemistry, food web, mesopelagic fish community) and a snapshot of...
CruiseReport.pdf
CruiseReport.pdf
Dec 8, 2021 - Stressor 2019: Cruise Report
Adobe PDF - 5.2 MB - MD5: 375ecfb6bd77471cb8e24e1fc1139bf5
Cruise report
Replication Data for: Closure Law Model Uncertainty Quantification
Dec 5, 2021 - NTNU – Norwegian University of Science and Technology
Strand, Andreas; Kjølaas, Jørn; Bergstrøm, Trond H.; Steinsland, Ingelin; Hellevik, Leif R., 2021, "Replication Data for: Closure Law Model Uncertainty Quantification", https://doi.org/10.18710/3OJHDN, DataverseNO, V1
The prediction uncertainty in simulators for industrial processes is due to uncertainties in the input variables and uncertainties in specification of the models, in particular the closure laws. In this work, the uncertainty in each closure law was modeled as a random variable and the parameters of its distribution were optimized to correctly quant...
00_ReadMe.txt
Dec 5, 2021 - Replication Data for: Closure Law Model Uncertainty Quantification
Plain Text - 828 B - MD5: fc098393ea1a0256a9d530355cb495fc
experiment.py
Dec 5, 2021 - Replication Data for: Closure Law Model Uncertainty Quantification
Python Source Code - 30.1 KB - MD5: 60d99f5b466ead54f16e330c1c51857b
gas_dominated.py
Dec 5, 2021 - Replication Data for: Closure Law Model Uncertainty Quantification
Python Source Code - 2.7 KB - MD5: 60ae3241fa833a7ffac649a68d5bc32d
liquid_dominated.py
Dec 5, 2021 - Replication Data for: Closure Law Model Uncertainty Quantification
Python Source Code - 3.0 KB - MD5: 877f2f0b9aaea09431332b1f2cf49607
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