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A taxonomy-free approach based on machine learning to assess the quality of rivers with diatoms.
Feio, Maria João; Serra, Sónia R Q; Mortágua, Andreia; Bouchez, Agnès; Rimet, Frédéric; Vasselon, Valentin; Almeida, Salomé F P.
Afiliación
  • Feio MJ; MARE - Marine and Environmental Sciences Centre, Department of Life Sciences, University of Coimbra, Portugal. Electronic address: mjf@ci.uc.pt.
  • Serra SRQ; MARE - Marine and Environmental Sciences Centre, Department of Life Sciences, University of Coimbra, Portugal.
  • Mortágua A; Department of Biology and Geobiotec - Geobiosciences, Geotechnologies and Geoengineering Research Centre, University of Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal.
  • Bouchez A; UMR CARRTEL, INRAE, Université Savoie Mont-Blanc, F-74200 Thonon, France.
  • Rimet F; UMR CARRTEL, INRAE, Université Savoie Mont-Blanc, F-74200 Thonon, France.
  • Vasselon V; Pôle R&D "ECLA", France; AFB, Site INRA UMR CARRTEL, Thonon-les-Bains, France.
  • Almeida SFP; Department of Biology and Geobiotec - Geobiosciences, Geotechnologies and Geoengineering Research Centre, University of Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal.
Sci Total Environ ; 722: 137900, 2020 Jun 20.
Article en En | MEDLINE | ID: mdl-32199386
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Diatomeas / Ríos Tipo de estudio: Prognostic_studies País/Región como asunto: Europa Idioma: En Revista: Sci Total Environ Año: 2020 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Diatomeas / Ríos Tipo de estudio: Prognostic_studies País/Región como asunto: Europa Idioma: En Revista: Sci Total Environ Año: 2020 Tipo del documento: Article