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共检索到226条 ,权限内显示50条;
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Data from: Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model
- 负责人:
- DOI:
- doi:10.5061/dryad.2hp1978
- 摘要:
- Traditional anatomical analyses captured only a fraction of real phenomic information. Here, we apply deep learning to quantify total p
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Quarternary deep-sea Ostracode taxonomy of ocean drilling program site 980, Easterm North Atlantic Ocean
- 负责人:
- DOI:
- doi:10.5061/dryad.55d46
- 摘要:
- for late Quaternary deep-sea ostracode taxonomy. Nineteen genera and 32 species were examined and (re-)illustrated with high-resolution scanni
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Accurate inference of tree topologies from multiple sequence alignments using deep learning
- 负责人:
- DOI:
- doi:10.5061/dryad.ct2895s
- 摘要:
- h as statistical inconsistency and/or the tendency to be positively misleading (i.e. assert strong support for the incorrect tree topology). Recently, deep learning
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry
- 负责人:
- DOI:
- doi:10.5061/dryad.7482v2n
- 摘要:
- the potential of a deep learning?based photogrammetry system for automatic species identification and measurement. We then present the same data analysed usi
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Taxonomic revision of deep-sea Ostracoda from the Arctic Ocean
- 负责人:
- DOI:
- doi:10.5061/dryad.r2170
- 摘要:
- with high-resolution scanning electron microscopy images, covering most of known deep-sea species in the central Arctic Ocean. Seven new species are describe
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Data from: A molecular gut content study of Themisto abyssorum (Amphipoda) from Arctic hydrothermal vent and cold seep systems
- 负责人:
- DOI:
- doi:10.5061/dryad.tm1k0
- 摘要:
- , correct deep-sea experimental laboratory conditions are difficult to obtain, animals rarely survive the sampling, or the study organisms feed duri
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The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology - DgoMB: Fish
- 负责人:
- 关键词:
- DOI:
- doi:10.15468/ohcmuc
- 摘要:
- kely targets of future resource exploration and exploitation. However, to develop a Gulf-wide perspective of deep-sea communities, sampling in areas beyond those
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Projecting the recovery of a long-lived deep-sea octocoral species after the Deepwater Horizon oil spill using structured population models
- 负责人:
- DOI:
- doi:10.5061/dryad.2d0g778
- 摘要:
- 1. Deep-water coral communities are hotspots of diversity and biomass in the deep sea. Most deep-sea coral species are long-lived and slow-growing
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Deep learning for bridge load capacity estimation in post-disaster and -conflict zones
- 负责人:
- DOI:
- doi:10.5061/dryad.6br51tn
- 摘要:
- can be costly and slow when expertise is lacking in challenging scenarios. In this paper, we propose to employ deep learning as method to estimate
![](http://agri.nais.net.cn/resources/front/images/source_91.jpg)
Data from: Importance of deep water uptake in tropical eucalypt forest
- 负责人:
- DOI:
- doi:10.5061/dryad.5kc40
- 摘要:
- role for large amounts of water stored during the wet season that is taken up by trees during dry periods. Our study confirms that deep rooting could