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[null,"1.","Record Nr.",null,"UNINA9910464766703321"]
[null,null,"Titolo",null,"Assessing risks to endangered and threatened species from pesticides / / Committee on Ecological Risk Assessment under FIFRA and ESA, Board on Environmental Studies and Toxicology, Division on Earth and Life Studies, National Research Council of the National Academies"]
[null,null,"Pubbl/distr/stampa",null,"Washington, DC : , : National Academies Press, , [2013]"]
[{"$nulls":4},"©2013"]
[null,null,"ISBN",null,"0-309-28584-4"]
[null,null,"Descrizione fisica",null,"1 online resource (195 p.)"]
[null,null,"Disciplina",null,"363.7384"]
[null,null,"Soggetti",null,"Pesticides and wildlife"]
[{"$nulls":4},"Endangered species"]
[{"$nulls":4},"Pesticides - Risk assessment"]
[{"$nulls":4},"Ecological risk assessment"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","\"Sponsors: National Oceanic and Atmospheric Administration, U.S. Department of Agriculture, U.S. Environmental Protection Agency, U.S. Fish and Wildlife Service\"--Page v."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Introduction -- A common approach and other overarching issues -- Exposure analysis -- Effects analysis -- Risk characterization -- Appendixes."]
[null,"2.","Record Nr.",null,"UNINA9910831849203321"]
[null,null,"Autore",null,"Hopf Konstantin"]
[null,null,"Titolo",null,"Predictive Analytics for Energy Efficiency and Energy Retailing / Konstantin Hopf . Volume 36"]
[null,null,"Pubbl/distr/stampa",null,"[s.l.] : , : Bamberg University Press, , 2019"]
[null,null,"ISBN",null,"9783863096694"]
[{"$nulls":4},"386309669X"]
[null,null,"Descrizione fisica",null,"1 online resource (1 p.)"]
[null,null,"Collana",null,"Schriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik"]
[null,null,"Soggetti",null,"Computers / Software Development & Engineering"]
[{"$nulls":4},"Computers"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","Digitization causes large amounts of data in organizations (e.g., transaction data from business processes, communication data, sensor data). Besides, a large number of data sources are emerging online and can be freely used. Firms are looking for ways to commercialize this increasing amount of data and research aims to better understand the data value creation process. The present dissertation answers five central research questions in this context and examines how machine learning (ML) can be used to create value from data, using case studies from energy retailing and energy efficiency. First, a systematic literature review gives an overview of firm internal and external data sources for potential analyses. Second, the importance of human cognition, theory, and expert knowledge in effective data preparation for ML is demonstrated. Third, current ML algorithms and variable selection methods are empirically compared using industry data sets. Implications for theory and practice are identified. Finally, the successful use of the information gained through ML is exemplified through case studies where increased energy efficiency, customer value, and service quality can demonstrate economic, environmental, and social value. Thus, this empirical work contributes to the so far rather conceptual discussion on value creation from big data in information systems research."]