Item talk:Q49493
From geokb
ORCID:
'@context': http://schema.org '@id': https://orcid.org/0000-0002-1818-355X '@reverse': creator: - '@id': https://doi.org/10.1016/j.envsoft.2021.105006 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.1016/j.envsoft.2021.105006 name: Evaluation of six methods for correcting bias in estimates from ensemble tree machine learning regression models - '@id': https://doi.org/10.1111/gwat.13063 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.1111/gwat.13063 name: Machine Learning Predictions of pH in the Glacial Aquifer System, Northern USA - '@id': https://doi.org/10.1029/2020wr028207 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.1029/2020wr028207 name: Machine Learning Predicted Redox Conditions in the Glacial Aquifer System, Northern Continental United States - '@id': https://doi.org/10.3133/sir20215069 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3133/sir20215069 name: Depth of groundwater used for drinking-water supplies in the United States - '@id': https://doi.org/10.1021/acs.est.0c00192 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.1021/acs.est.0c00192 name: Occurrence and Geochemistry of Lead-210 and Polonium-210 Radionuclides in Public-Drinking-Water Supplies from Principal Aquifers of the United States - '@id': https://doi.org/10.1016/j.jhydrol.2019.124505 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.1016/j.jhydrol.2019.124505 name: Environmental tracer evidence for connection between shallow and bedrock aquifers and high intrinsic susceptibility to contamination of the conterminous U.S. glacial aquifer - '@id': https://doi.org/10.1016/j.apgeochem.2017.11.002 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.1016/j.apgeochem.2017.11.002 name: Radium mobility and the age of groundwater in public-drinking-water supplies from the Cambrian-Ordovician aquifer system, north-central USA - '@id': https://doi.org/10.3133/ds1087 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3133/ds1087 name: Groundwater-quality and select quality-control data from the National Water-Quality Assessment Project, January through December 2015, and previously unpublished data from 2013 to 2014 - '@id': https://doi.org/10.3133/fs20173056 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3133/fs20173056 name: Groundwater quality in the Cambrian-Ordovician aquifer system, midwestern United States - '@id': https://doi.org/10.3133/fs20173055 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3133/fs20173055 name: Groundwater quality in the glacial aquifer system, United States - '@id': https://doi.org/10.3133/ds1063 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3133/ds1063 name: Groundwater-quality data from the National Water-Quality Assessment Project, January through December 2014 and select quality-control data from May 2012 through December 2014 - '@id': https://doi.org/10.3133/sir20055287 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3133/sir20055287 name: Development and application of a regression equation for estimating the occurrence of atrazine in shallow ground water beneath agricultural areas of the United States - '@id': https://doi.org/10.3133/cir1291 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3133/cir1291 name: "Pesticides in the Nation's Streams and Ground Water, 1992\u20132001" - '@id': https://doi.org/10.3133/sir20045174 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3133/sir20045174 name: Application of health-based screening levels to ground-water quality data in a state-scale pilot effort - '@id': https://doi.org/10.3133/ofr01378 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3133/ofr01378 name: Water quality data for selected wells in the Coastal Plain of New Jersey, 1996-98 - '@id': https://doi.org/10.3133/cir1201 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3133/cir1201 name: "Water quality in the Long Island-New Jersey coastal drainages, New York\ \ and New Jersey, 1996\u201398" - '@id': https://doi.org/10.3133/fs11896 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3133/fs11896 name: Presence and distribution of chlorinated organic compounds in streambed sediments, New Jersey - '@id': https://doi.org/10.3133/fs01294 '@type': CreativeWork identifier: '@type': PropertyValue propertyID: doi value: 10.3133/fs01294 name: National Water-Quality Assessment (NAWQA) Program, Long Island-New Jersey (LINJ) Coastal Drainages Study Unit '@type': Person affiliation: '@id': https://doi.org/10.13039/100000201 '@type': Organization alternateName: U.S. Department of the Interior name: U.S. Geological Survey familyName: Stackelberg givenName: Paul mainEntityOfPage: https://orcid.org/0000-0002-1818-355X
USGS Staff Profile:
'@context': https://schema.org '@type': Person affiliation: [] description: - '@type': TextObject abstract: Hydrologist with the Water Resources Mission Area additionalType: short description - '@type': TextObject abstract: Paul Stackelberg is a Hydrologist with the Water Resources Mission Area. additionalType: staff profile page introductory statement - '@type': TextObject abstract: 'Paul Stackelberg has worked as a hydrologist with the U.S. Geological Survey (USGS) since 1988. His research interests have included (1) evaluating natural and anthropogenic factors that affect groundwater quality, (2) developing statistical models for predicting the occurrence of contaminants in groundwater resources, and (3) determining the persistence and fate of pharmaceuticals and other wastewater-related compounds in conventional and advanced drinking-water-treatment facilities. Currently Paul is leading a team of USGS scientist who are using machine learning methods to predict groundwater quality conditions in three dimensions throughout select principal aquifers of the United States as well as at the depths commonly used for domestic and public supplies at the National scale.Education:M.S., Geology, University of Missouri - Columbia.B.S., Geology and Mineralology, The Ohio State University, Minor: Computer and Information Science.' additionalType: personal statement email: pestack@usgs.gov hasCredential: [] hasOccupation: - '@type': OrganizationalRole affiliatedOrganization: '@type': Organization name: Water Resources Mission Area url: https://www.usgs.gov/mission-areas/water-resources roleName: Hydrologist startDate: '2024-05-12T15:40:53.967491' identifier: - '@type': PropertyValue propertyID: GeoKB value: https://geokb.wikibase.cloud/entity/Q49493 - '@type': PropertyValue propertyID: ORCID value: 0000-0002-1818-355X jobTitle: Hydrologist knowsAbout: - '@type': Thing additionalType: self-claimed expertise name: machine learning - '@type': Thing additionalType: self-claimed expertise name: groundwater quality memberOf: '@type': OrganizationalRole member: '@type': Organization name: U.S. Geological Survey name: staff member startDate: '2024-05-12T15:40:53.964906' name: Paul Stackelberg url: https://www.usgs.gov/staff-profiles/paul-stackelberg
