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Article: Addressing the Data Scarcity Problem in Ecotoxicology via Small Data Machine Learning Methods

TitleAddressing the Data Scarcity Problem in Ecotoxicology via Small Data Machine Learning Methods
Authors
Wang, YingDong, JinchuZhou, YunchiCheng, YinghaoZhao, XiaoliPeijnenburg, Willie J.G.M.Vijver, Martina G.Leung, Kenneth M.Y.Fan, WenhongWu, Fengchang
Keywordsartificial intelligence
data augmentation
ecotoxicity
modeling workflow
prediction
small data machine learning (SDML)
Issue Date20-Mar-2025
PublisherAmerican Chemical Society
Citation
Environmental Science and Technology, 2025, v. 59, n. 12, p. 5867-5871 How to Cite?
DOI: http://dx.doi.org/10.1021/acs.est.5c00510
Persistent Identifierhttp://hdl.handle.net/10722/367326
ISSN
0013-936X
2023 Impact Factor: 10.8
2023 SCImago Journal Rankings: 3.516

 

DC FieldValueLanguage
dc.contributor.authorWang, Ying-
dc.contributor.authorDong, Jinchu-
dc.contributor.authorZhou, Yunchi-
dc.contributor.authorCheng, Yinghao-
dc.contributor.authorZhao, Xiaoli-
dc.contributor.authorPeijnenburg, Willie J.G.M.-
dc.contributor.authorVijver, Martina G.-
dc.contributor.authorLeung, Kenneth M.Y.-
dc.contributor.authorFan, Wenhong-
dc.contributor.authorWu, Fengchang-
dc.date.accessioned2025-12-10T08:06:33Z-
dc.date.available2025-12-10T08:06:33Z-
dc.date.issued2025-03-20-
dc.identifier.citationEnvironmental Science and Technology, 2025, v. 59, n. 12, p. 5867-5871-
dc.identifier.issn0013-936X-
dc.identifier.urihttp://hdl.handle.net/10722/367326-
dc.languageeng-
dc.publisherAmerican Chemical Society-
dc.relation.ispartofEnvironmental Science and Technology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectartificial intelligence-
dc.subjectdata augmentation-
dc.subjectecotoxicity-
dc.subjectmodeling workflow-
dc.subjectprediction-
dc.subjectsmall data machine learning (SDML)-
dc.titleAddressing the Data Scarcity Problem in Ecotoxicology via Small Data Machine Learning Methods-
dc.typeArticle-
dc.identifier.doi10.1021/acs.est.5c00510-
dc.identifier.pmid40111220-
dc.identifier.scopuseid_2-s2.0-105000483309-
dc.identifier.volume59-
dc.identifier.issue12-
dc.identifier.spage5867-
dc.identifier.epage5871-
dc.identifier.eissn1520-5851-
dc.identifier.issnl0013-936X-

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