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Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.

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Title: Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.
Authors: Greenhill S; Department of Agricultural and Resource Economics, University of California, Berkeley, Berkeley, CA 94720, USA.; Goldman School of Public Policy, University of California, Berkeley, Berkeley, CA 94720, USA., Druckenmiller H; Resources for the Future, Washington, DC 20036, USA.; Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA., Wang S; Goldman School of Public Policy, University of California, Berkeley, Berkeley, CA 94720, USA.; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.; Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139, USA., Keiser DA; Department of Resource Economics, University of Massachusetts, Amherst, Amherst, MA 010013, USA.; Center for Agricultural and Rural Development, Iowa State University, Ames, IA 50011, USA.; National Bureau of Economic Research, Cambridge, MA 02139, USA., Girotto M; Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA 94720, USA., Moore JK; US Department of Energy, Washington, DC 20585, USA., Yamaguchi N; School of Information, University of California, Berkeley, Berkeley, CA 94720, USA., Todeschini A; School of Information, University of California, Berkeley, Berkeley, CA 94720, USA., Shapiro JS; Department of Agricultural and Resource Economics, University of California, Berkeley, Berkeley, CA 94720, USA.; National Bureau of Economic Research, Cambridge, MA 02139, USA.; Department of Economics, University of California, Berkeley, Berkeley, CA 94720, USA.
Source: Science (New York, N.Y.) [Science] 2024 Jan 26; Vol. 383 (6681), pp. 406-412. Date of Electronic Publication: 2024 Jan 25.
Publication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
Journal Info: Publisher: American Association for the Advancement of Science Country of Publication: United States NLM ID: 0404511 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-9203 (Electronic) Linking ISSN: 00368075 NLM ISO Abbreviation: Science Subsets: MEDLINE
Database: MEDLINE Ultimate
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  Data: Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates.
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  Data: <searchLink fieldCode="AU" term="%22Greenhill+S%22">Greenhill S</searchLink>; Department of Agricultural and Resource Economics, University of California, Berkeley, Berkeley, CA 94720, USA.; Goldman School of Public Policy, University of California, Berkeley, Berkeley, CA 94720, USA.<br /><searchLink fieldCode="AU" term="%22Druckenmiller+H%22">Druckenmiller H</searchLink>; Resources for the Future, Washington, DC 20036, USA.; Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA.<br /><searchLink fieldCode="AU" term="%22Wang+S%22">Wang S</searchLink>; Goldman School of Public Policy, University of California, Berkeley, Berkeley, CA 94720, USA.; Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.; Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.<br /><searchLink fieldCode="AU" term="%22Keiser+DA%22">Keiser DA</searchLink>; Department of Resource Economics, University of Massachusetts, Amherst, Amherst, MA 010013, USA.; Center for Agricultural and Rural Development, Iowa State University, Ames, IA 50011, USA.; National Bureau of Economic Research, Cambridge, MA 02139, USA.<br /><searchLink fieldCode="AU" term="%22Girotto+M%22">Girotto M</searchLink>; Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA 94720, USA.<br /><searchLink fieldCode="AU" term="%22Moore+JK%22">Moore JK</searchLink>; US Department of Energy, Washington, DC 20585, USA.<br /><searchLink fieldCode="AU" term="%22Yamaguchi+N%22">Yamaguchi N</searchLink>; School of Information, University of California, Berkeley, Berkeley, CA 94720, USA.<br /><searchLink fieldCode="AU" term="%22Todeschini+A%22">Todeschini A</searchLink>; School of Information, University of California, Berkeley, Berkeley, CA 94720, USA.<br /><searchLink fieldCode="AU" term="%22Shapiro+JS%22">Shapiro JS</searchLink>; Department of Agricultural and Resource Economics, University of California, Berkeley, Berkeley, CA 94720, USA.; National Bureau of Economic Research, Cambridge, MA 02139, USA.; Department of Economics, University of California, Berkeley, Berkeley, CA 94720, USA.
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  Data: <searchLink fieldCode="JN" term="%220404511%22">Science (New York, N.Y.)</searchLink> [Science] 2024 Jan 26; Vol. 383 (6681), pp. 406-412. <i>Date of Electronic Publication: </i>2024 Jan 25.
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              Text: 2024 Jan 26
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