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科研机构
海洋研究所 [5]
沈阳自动化研究所 [1]
内容类型
期刊论文 [6]
发表日期
2020 [6]
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Assessments of 16 Exploited Fish Stocks in Chinese Waters Using the CMSY and BSM Methods
期刊论文
FRONTIERS IN MARINE SCIENCE, 2020, 卷号: 7, 页码: 10
作者:
Zhai, Lu
;
Liang, Cui
;
Pauly, Daniel
收藏
  |  
浏览/下载:14/0
  |  
提交时间:2021/04/12
data-poor fisheries
Chinese coastal fisheries
biomass declines
CMSY
stock assessments
Assessment of 11 Exploited Fish and Invertebrate Populations in the Japan Sea Using the CMSY and BSM Methods
期刊论文
FRONTIERS IN MARINE SCIENCE, 2020, 卷号: 7, 页码: 10
作者:
Zhang, Shanshan
;
Wang, Yibang
;
Wang, Yuanchao
;
Liang, Cui
;
Xian, Weiwei
收藏
  |  
浏览/下载:40/0
  |  
提交时间:2021/04/12
data-limited stock assessment
maximum sustainable yield
fisheries management
the Japan Sea and environs
CMSY and BSM
Stock Status Assessments for 12 Exploited Fishery Species in the Tsushima Warm Current Region, Southwest Japan and East China, Using the CMSY and BSM Methods
期刊论文
FRONTIERS IN MARINE SCIENCE, 2020, 卷号: 7, 页码: 11
作者:
Wang, Yuanchao
;
Liang, Cui
;
Wang, Yibang
;
Xian, Weiwei
;
Palomares, Maria Lourdes
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  |  
浏览/下载:16/0
  |  
提交时间:2020/10/26
CMSY
BSM
fishery status
reference points
stock assessments
Assessment of 12 Fish Species in the Northwest Pacific Using the CMSY and BSM Methods
期刊论文
FRONTIERS IN MARINE SCIENCE, 2020, 卷号: 7, 页码: 11
作者:
Wang, Yibang
;
Wang, Yuanchao
;
Liang, Cui
;
Zhang, Hui
;
Xian, Weiwei
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  |  
浏览/下载:16/0
  |  
提交时间:2020/09/25
east coast of Japan
overfishing
stock assessment
CMSY/BSM methods
data-sparse stock
Assessments of 15 Exploited Fish Stocks in Chinese, South Korean and Japanese Waters Using the CMSY and BSM Methods
期刊论文
FRONTIERS IN MARINE SCIENCE, 2020, 卷号: 7, 页码: 11
作者:
Liang, Cui
;
Xian, Weiwei
;
Pauly, Daniel
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  |  
浏览/下载:10/0
  |  
提交时间:2020/09/25
data-sparse stock
North-East Asia
CMSY
BSM
stock assessment
Learning Adaptive Semi-Supervised Multi-Output Soft-Sensors with Co-Training of Heterogeneous Models
期刊论文
IEEE Access, 2020, 卷号: 8, 页码: 46493-46504
作者:
Li, Dong
;
Huang DP(黄道平)
;
Yu GP(于广平)
;
Liu YQ(刘乙奇)
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浏览/下载:11/0
  |  
提交时间:2020/04/11
Soft-sensors, semi-supervised
co-training, multi-output
recursive partial least square (RPLS)
long short-term memory recurrent neural network (LSTM)
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