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Quantifying cell densities and biovolumes of phytoplankton communities and functional groups using scanning flow cytometry, machine learning and unsupervised clustering.
Thomas, Mridul K; Fontana, Simone; Reyes, Marta; Pomati, Francesco.
Affiliation
  • Thomas MK; Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.
  • Fontana S; Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.
  • Reyes M; Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.
  • Pomati F; Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland.
PLoS One ; 13(5): e0196225, 2018.
Article in En | MEDLINE | ID: mdl-29746500
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Phytoplankton / Algorithms / Ecosystem / Flow Cytometry / Machine Learning Type of study: Prognostic_studies Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2018 Document type: Article Affiliation country: Country of publication:
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Phytoplankton / Algorithms / Ecosystem / Flow Cytometry / Machine Learning Type of study: Prognostic_studies Language: En Journal: PLoS One Journal subject: CIENCIA / MEDICINA Year: 2018 Document type: Article Affiliation country: Country of publication: