LEADER 02543nam 2200637 450 001 9910464766703321 005 20200520144314.0 010 $a0-309-28584-4 035 $a(CKB)3710000000103367 035 $a(EBL)3379230 035 $a(SSID)ssj0001213729 035 $a(PQKBManifestationID)11722732 035 $a(PQKBTitleCode)TC0001213729 035 $a(PQKBWorkID)11270932 035 $a(PQKB)10385335 035 $a(MiAaPQ)EBC3379230 035 $a(Au-PeEL)EBL3379230 035 $a(CaPaEBR)ebr10863884 035 $a(OCoLC)923289746 035 $a(EXLCZ)993710000000103367 100 $a20140328h20132013 uy| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aAssessing risks to endangered and threatened species from pesticides /$fCommittee on Ecological Risk Assessment under FIFRA and ESA, Board on Environmental Studies and Toxicology, Division on Earth and Life Studies, National Research Council of the National Academies 210 1$aWashington, DC :$cNational Academies Press,$d[2013] 210 4$d©2013 215 $a1 online resource (195 p.) 300 $a"Sponsors: National Oceanic and Atmospheric Administration, U.S. Department of Agriculture, U.S. Environmental Protection Agency, U.S. Fish and Wildlife Service"--Page v. 311 $a0-309-28583-6 320 $aIncludes bibliographical references. 327 $aIntroduction -- A common approach and other overarching issues -- Exposure analysis -- Effects analysis -- Risk characterization -- Appendixes. 606 $aPesticides and wildlife 606 $aEndangered species 606 $aPesticides$xRisk assessment 606 $aEcological risk assessment 608 $aElectronic books. 615 0$aPesticides and wildlife. 615 0$aEndangered species. 615 0$aPesticides$xRisk assessment. 615 0$aEcological risk assessment. 676 $a363.7384 712 02$aUnited States.$bNational Oceanic and Atmospheric Administration, 712 02$aUnited States.$bDepartment of Agriculture, 712 02$aUnited States.$bEnvironmental Protection Agency, 712 02$aU.S. Fish and Wildlife Service, 712 02$aNational Research Council (U.S.).$bBoard on Environmental Studies and Toxicology, 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910464766703321 996 $aAssessing risks to endangered and threatened species from pesticides$92193796 997 $aUNINA LEADER 03296nam 2200661 a 450 001 9910789753203321 005 20230725032239.0 010 $a1-283-25992-3 010 $a9786613259929 010 $a90-485-2148-3 035 $a(CKB)2670000000114336 035 $a(EBL)770897 035 $a(OCoLC)751962288 035 $a(SSID)ssj0000637193 035 $a(PQKBManifestationID)12255508 035 $a(PQKBTitleCode)TC0000637193 035 $a(PQKBWorkID)10677693 035 $a(PQKB)10549758 035 $a(MiAaPQ)EBC770897 035 $a(Au-PeEL)EBL770897 035 $a(CaPaEBR)ebr10498844 035 $a(CaONFJC)MIL325992 035 $a(EXLCZ)992670000000114336 100 $a20111105d2010 uy 0 101 0 $adut 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aInzicht en toezicht$b[electronic resource] /$fGerard Alberts 210 $aAmsterdam $cAksant$d2010 215 $a1 online resource (206 p.) 225 0 $aJaarboek KennisSamenleving,$x1871-0034 ;$vd. 6, 2010 300 $aDescription based upon print version of record. 311 $a90-5260-384-7 320 $aIncludes bibliographical references. 327 $aInzicht en toezicht; Inhoud; 1 Inzicht en toezicht Controle in de kennissamenleving; 2 Het wakende oog; 3 Tjebbe Beekmans verbeelding van toezicht; 4 Zitdwang; 5 Ode aan Lau Mazirel; 6 De maximaal beveiligde samenleving en nieuwe surveillancetechnieken; 7 Verloren onschuld. Inzicht en Toezicht binnen de Verwijsindex Ri sicojongeren; 8 Inzicht in toezicht: toezicht door inzicht; 9 Kennis in een toezichtmaatschappij: Geautomatiseerde mensenkennis; 10 De digitale spiegelwereld draait door Een maatschappelijk perspectief op Google Earth 327 $a11 'I always feel like somebody's watching me' Van surveillance naar coveillance12 TOEZICHT IN DE VOORZORGSTAAT Kennis en informatiegebruik tussen staatscontrole en sociabiliteit; 13 Overheids- en burgertoezicht in de kennissamenleving: pleidooi voor een LAT -relatie; 14 Over de redactie; 15 Over de auteurs 330 $aNieuwe technieken maken het opslaan en verwerken van informatie eenvoudiger. Sterker nog: ze maken de weg vrij om gegevens vast te leggen voordat duidelijk is welk inzicht we eigenlijk nastreven. Toezicht wint zo terrein ten opzichte van de kennisverwerving. Voorbeelden van toenemend toezicht zijn elektronische dossiers over burgers, informatievergaring door de politie en in private initiatieven zoals Google Earth. Deze systemen maken het mogelijk om op grote schaal verbanden te ontdekken en afwijkingen op te sporen, zonder dat er duidelijke kennisvragen aan vooraf gaan. De relatie tussen inzi 410 0$aJaarboek KennisSamenleving, 6 606 $aKnowledge, Sociology of 606 $aKnowledge management 606 $aSupervision 606 $aElectronic surveillance 615 0$aKnowledge, Sociology of. 615 0$aKnowledge management. 615 0$aSupervision. 615 0$aElectronic surveillance. 676 $a320 676 $a900 700 $aAlberts$b Gerard$01104954 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910789753203321 996 $aInzicht en toezicht$93719384 997 $aUNINA LEADER 02786nam 22004093a 450 001 9910831849203321 005 20250203235540.0 010 $a9783863096694 010 $a386309669X 024 8 $ahttps://doi.org/10.20378/irbo-54833 035 $a(CKB)4950000000290237 035 $a(ScCtBLL)33be3fb7-89e7-435d-a5c0-6fc564fcf52f 035 $a(Perlego)2327436 035 $a(EXLCZ)994950000000290237 100 $a20250203i20192021 uu 101 0 $aeng 135 $auru|||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aPredictive Analytics for Energy Efficiency and Energy Retailing$fKonstantin Hopf$hVolume 36 210 1$a[s.l.] :$cBamberg University Press,$d2019. 215 $a1 online resource (1 p.) 225 1 $aSchriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik 330 $aDigitization 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. 410 $aSchriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik 606 $aComputers / Software Development & Engineering$2bisacsh 606 $aComputers 615 7$aComputers / Software Development & Engineering 615 0$aComputers. 700 $aHopf$b Konstantin$01786935 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910831849203321 996 $aPredictive Analytics for Energy Efficiency and Energy Retailing$94319467 997 $aUNINA