Global Patterns and Predictions of Seafloor Biomass Using Random Forests

A comprehensive seafloor biomass and abundance database has been constructed from 24 oceanographic institutions worldwide within the Census of Marine Life (CoML) field projects. The machine-learning algorithm, Random Forests, was employed to model and predict seafloor standing stocks from surface pr...

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Detalles Bibliográficos
Autores principales: Wei, Chih-Lin, Rowe, Gilbert T., Escobar-Briones, Elva, Boetius, Antje, Soltwedel, Thomas, Caley, M. Julian, Soliman, Yousria, Huettmann, Falk, Qu, Fangyuan, Yu, Zishan, Pitcher, C. Roland, Haedrich, Richard L., Wicksten, Mary K., Rex, Michael A., Baguley, Jeffrey G., Sharma, Jyotsna, Danovaro, Roberto, MacDonald, Ian R., Nunnally, Clifton C., Deming, Jody W., Montagna, Paul, Lévesque, Mélanie, Weslawski, Jan Marcin, Wlodarska-Kowalczuk, Maria, Ingole, Baban S., Bett, Brian J., Billett, David S. M., Yool, Andrew, Bluhm, Bodil A., Iken, Katrin, Narayanaswamy, Bhavani E.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2010
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3012679/
https://www.ncbi.nlm.nih.gov/pubmed/21209928
http://dx.doi.org/10.1371/journal.pone.0015323

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3012679/
https://www.ncbi.nlm.nih.gov/pubmed/21209928
http://dx.doi.org/10.1371/journal.pone.0015323

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