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Artificial intelligence models to predict acute phytotoxicity in petroleum contaminated soils.
Shadrin, Dmitrii; Pukalchik, Mariia; Kovaleva, Ekaterina; Fedorov, Maxim.
Affiliation
  • Shadrin D; Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 143026, Moscow, Russia. Electronic address: Dmitry.Shadrin@skolkovotech.ru.
  • Pukalchik M; Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 143026, Moscow, Russia. Electronic address: m.pukalchik@skoltech.ru.
  • Kovaleva E; Faculty of Soil Science,Lomonosov Moscow State University, 119991, Moscow, Russia.
  • Fedorov M; Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, 143026, Moscow, Russia.
Ecotoxicol Environ Saf ; 194: 110410, 2020 May.
Article in En | MEDLINE | ID: mdl-32163774
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Soil Pollutants / Hordeum / Petroleum / Artificial Intelligence / Hydrocarbons / Models, Theoretical Type of study: Prognostic_studies / Risk_factors_studies Country/Region as subject: Asia / Europa Language: En Journal: Ecotoxicol Environ Saf Year: 2020 Document type: Article Country of publication: Países Bajos
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Soil Pollutants / Hordeum / Petroleum / Artificial Intelligence / Hydrocarbons / Models, Theoretical Type of study: Prognostic_studies / Risk_factors_studies Country/Region as subject: Asia / Europa Language: En Journal: Ecotoxicol Environ Saf Year: 2020 Document type: Article Country of publication: Países Bajos