Application of machine learning methods to restore size-sex composition in catches of snow crab

Impact of environmental conditions on spatial distribution of size-sex groups is described statistically for snow crab Chionoecetes opilio. Based on the relationships identified, absolute values of catches are calculated for each such group. Machine learning approach is implemented for the feature e...

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Main Authors: I. S. Chernienko, A. G. Slizkin, V. V. Kulik
Format: Article
Language:Russian
Published: Transactions of the Pacific Research Institute of Fisheries and Oceanography 2024-12-01
Series:Известия ТИНРО
Subjects:
Online Access:https://izvestiya.tinro-center.ru/jour/article/view/1008
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author I. S. Chernienko
A. G. Slizkin
V. V. Kulik
author_facet I. S. Chernienko
A. G. Slizkin
V. V. Kulik
author_sort I. S. Chernienko
collection DOAJ
description Impact of environmental conditions on spatial distribution of size-sex groups is described statistically for snow crab Chionoecetes opilio. Based on the relationships identified, absolute values of catches are calculated for each such group. Machine learning approach is implemented for the feature engineering and statistical relationships evaluation. The approach can be adopted for other benthic stocks.
format Article
id doaj-art-a10134d3ce2d46598b4e39a7810601c3
institution DOAJ
issn 1606-9919
2658-5510
language Russian
publishDate 2024-12-01
publisher Transactions of the Pacific Research Institute of Fisheries and Oceanography
record_format Article
series Известия ТИНРО
spelling doaj-art-a10134d3ce2d46598b4e39a7810601c32025-08-20T02:54:36ZrusTransactions of the Pacific Research Institute of Fisheries and OceanographyИзвестия ТИНРО1606-99192658-55102024-12-0120441018103410.26428/1606-9919-2024-204-1018-1034870Application of machine learning methods to restore size-sex composition in catches of snow crabI. S. Chernienko0A. G. Slizkin1V. V. Kulik2Pacific branch of VNIRO (TINRO)Pacific branch of VNIRO (TINRO)Pacific branch of VNIRO (TINRO)Impact of environmental conditions on spatial distribution of size-sex groups is described statistically for snow crab Chionoecetes opilio. Based on the relationships identified, absolute values of catches are calculated for each such group. Machine learning approach is implemented for the feature engineering and statistical relationships evaluation. The approach can be adopted for other benthic stocks.https://izvestiya.tinro-center.ru/jour/article/view/1008bering seasnow crab<i>chionoecetes opilio</i>catch compositionhabitat environmentmachine learning
spellingShingle I. S. Chernienko
A. G. Slizkin
V. V. Kulik
Application of machine learning methods to restore size-sex composition in catches of snow crab
Известия ТИНРО
bering sea
snow crab
<i>chionoecetes opilio</i>
catch composition
habitat environment
machine learning
title Application of machine learning methods to restore size-sex composition in catches of snow crab
title_full Application of machine learning methods to restore size-sex composition in catches of snow crab
title_fullStr Application of machine learning methods to restore size-sex composition in catches of snow crab
title_full_unstemmed Application of machine learning methods to restore size-sex composition in catches of snow crab
title_short Application of machine learning methods to restore size-sex composition in catches of snow crab
title_sort application of machine learning methods to restore size sex composition in catches of snow crab
topic bering sea
snow crab
<i>chionoecetes opilio</i>
catch composition
habitat environment
machine learning
url https://izvestiya.tinro-center.ru/jour/article/view/1008
work_keys_str_mv AT ischernienko applicationofmachinelearningmethodstorestoresizesexcompositionincatchesofsnowcrab
AT agslizkin applicationofmachinelearningmethodstorestoresizesexcompositionincatchesofsnowcrab
AT vvkulik applicationofmachinelearningmethodstorestoresizesexcompositionincatchesofsnowcrab