A Deep Learning Model to Recognize and Quantitatively Analyze Cold Seep Substrates and the Dominant Associated Species
Characterizing habitats and species distribution is important to understand the structure and function of cold seep ecosystems. This paper develops a deep learning model for the fast and accurate recognition and classification of substrates and the dominant associated species in cold seeps. Consider...
Guardado en:
Autores principales: | Haining Wang, Xiaoxue Fu, Chengqian Zhao, Zhendong Luan, Chaolun Li |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/6fba8cbba4324a17888f93f7fa5541c4 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Production of Labile Protein-Like Dissolved Organic Carbon Associated With Anaerobic Methane Oxidization in the Haima Cold Seeps, South China Sea
por: Tingcang Hu, et al.
Publicado: (2021) -
Genome Reduction and Microbe-Host Interactions Drive Adaptation of a Sulfur-Oxidizing Bacterium Associated with a Cold Seep Sponge
por: Ren-Mao Tian, et al.
Publicado: (2017) -
Distinct Bottom-Water Bacterial Communities at Methane Seeps With Various Seepage Intensities in Haima, South China Sea
por: Xiaopeng Li, et al.
Publicado: (2021) -
BIOGEOGRAPHY OF DEEP-WATER CHEMOSYNTHETIC ECOSYSTEMS (CHESS): EXPLORING THE SOUTHERN OCEANS
por: Ramírez Llodra,Eva, et al.
Publicado: (2003) -
The occurrence of the parasitic amphipod Trischizostoma crosnieri (Amphipoda: Amphilochidea: Lysianassida) in a methane seep site in the southeastern Pacific
por: Pérez-Schultheiss,Jorge, et al.
Publicado: (2021)