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

Feio, Maria João; Serra, Sónia R Q; Mortágua, Andreia; Bouchez, Agnès; Rimet, Frédéric; Vasselon, Valentin; Almeida, Salomé F P.

Affiliation

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 de En | MEDLINE | ID: mdl-32199386

Sujet(s)
Mots clés

Bioassessment; HYDRA; Machine learning; Metabarcoding; OTUs; Rivers

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Diatomées / Rivières Type d'étude: Prognostic_studies Pays/Région comme sujet: Europa Langue: En Journal: Sci Total Environ Année: 2020 Type de document: Article Pays de publication: Pays-Bas

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