Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies

scientific article published on 17 March 2021

Machine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies is …
instance of (P31):
scholarly articleQ13442814

External links are
P356DOI10.1021/ACS.EST.0C05239
P932PMC publication ID8852770
P698PubMed publication ID33729798

P50authorJoseph D. AyotteQ57260299
Paul M BradleyQ57414971
Matthew O GribbleQ57941080
Daniel K JonesQ59818353
Melissa LombardQ89362706
Patricia L ToccalinoQ104467510
P2093author name stringDebra T Silverman
Lorraine C Backer
Molly Scannell Bryan
Maria Argos
Michael J Focazio
Catherine Bulka
P2860cites workThe exposome and health: Where chemistry meets biologyQ92889146
P275copyright licenseCreative Commons Attribution-NonCommercial-NoDerivs 4.0 InternationalQ24082749
P6216copyright statuscopyrightedQ50423863
P4510describes a project that usesArcGISQ513297
P433issue8
P921main subjectexposure assessmentQ4008388
machine learningQ2539
P304page(s)5012-5023
P577publication date2021-03-17
P1433published inEnvironmental Science & TechnologyQ689027
P1476titleMachine Learning Models of Arsenic in Private Wells Throughout the Conterminous United States As a Tool for Exposure Assessment in Human Health Studies
P478volume55

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