Predicting the occurrence of short-chain PFAS in groundwater using machine-learned Bayesian networks

Runwei Li, Jacqueline MacDonald Gibson

Open source

DOI: 10.3389/fenvs.2022.958784
Published: 2022-11-03
Container: Frontiers in Environmental Science
Publisher: Frontiers Media SA
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.3389/fenvs.2022.958784,
  title = {Predicting the occurrence of short-chain PFAS in groundwater using machine-learned Bayesian networks},
  author = {Runwei Li and Jacqueline MacDonald Gibson},
  year = {2022},
  journal = {Frontiers in Environmental Science},
  doi = {10.3389/fenvs.2022.958784},
  url = {https://doi.org/10.3389/fenvs.2022.958784}
}

RIS

TY  - JOUR
TI  - Predicting the occurrence of short-chain PFAS in groundwater using machine-learned Bayesian networks
AU  - Runwei Li
AU  - Jacqueline MacDonald Gibson
PY  - 2022
JO  - Frontiers in Environmental Science
DO  - 10.3389/fenvs.2022.958784
UR  - https://doi.org/10.3389/fenvs.2022.958784
ER  -

APA

Li, R., & Gibson, J. M. (2022). Predicting the occurrence of short-chain PFAS in groundwater using machine-learned Bayesian networks. Frontiers in Environmental Science. https://doi.org/10.3389/fenvs.2022.958784

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crossref · retrieved 2026-09-25T10:19:57.654Z