scholarly article | Q13442814 |
P50 | author | Joseph D. Ayotte | Q57260299 |
Paul M Bradley | Q57414971 | ||
Matthew O Gribble | Q57941080 | ||
Daniel K Jones | Q59818353 | ||
Melissa Lombard | Q89362706 | ||
Patricia L Toccalino | Q104467510 | ||
P2093 | author name string | Debra T Silverman | |
Lorraine C Backer | |||
Molly Scannell Bryan | |||
Maria Argos | |||
Michael J Focazio | |||
Catherine Bulka | |||
P2860 | cites work | The exposome and health: Where chemistry meets biology | Q92889146 |
P275 | copyright license | Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International | Q24082749 |
P6216 | copyright status | copyrighted | Q50423863 |
P4510 | describes a project that uses | ArcGIS | Q513297 |
P433 | issue | 8 | |
P921 | main subject | exposure assessment | Q4008388 |
machine learning | Q2539 | ||
P304 | page(s) | 5012-5023 | |
P577 | publication date | 2021-03-17 | |
P1433 | published in | Environmental Science & Technology | Q689027 |
P1476 | title | Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies | |
P478 | volume | 55 |
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