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Karbalaeiramezanali, Amirabbas, Yousefi, Fazilat, Lentz, David R., Thorne, Kathleen G. (2025) Machine Learning Classification of Fertile and Barren Adakites for Refining Mineral Prospectivity Mapping: Geochemical Insights from the Northern Appalachians, New Brunswick, Canada. Minerals, 15 (4). doi:10.3390/min15040372

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Reference TypeJournal (article/letter/editorial)
TitleMachine Learning Classification of Fertile and Barren Adakites for Refining Mineral Prospectivity Mapping: Geochemical Insights from the Northern Appalachians, New Brunswick, Canada
JournalMinerals
AuthorsKarbalaeiramezanali, AmirabbasAuthor
Yousefi, FazilatAuthor
Lentz, David R.Author
Thorne, Kathleen G.Author
Year2025 (April 2)Volume15
Issue4
PublisherMDPI AG
DOIdoi:10.3390/min15040372Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID18268669Long-form Identifiermindat:1:5:18268669:0
GUID0
Full ReferenceKarbalaeiramezanali, Amirabbas, Yousefi, Fazilat, Lentz, David R., Thorne, Kathleen G. (2025) Machine Learning Classification of Fertile and Barren Adakites for Refining Mineral Prospectivity Mapping: Geochemical Insights from the Northern Appalachians, New Brunswick, Canada. Minerals, 15 (4). doi:10.3390/min15040372
Plain TextKarbalaeiramezanali, Amirabbas, Yousefi, Fazilat, Lentz, David R., Thorne, Kathleen G. (2025) Machine Learning Classification of Fertile and Barren Adakites for Refining Mineral Prospectivity Mapping: Geochemical Insights from the Northern Appalachians, New Brunswick, Canada. Minerals, 15 (4). doi:10.3390/min15040372
In(2025, March) Minerals Vol. 15 (4). MDPI AG

References Listed

These are the references the publisher has listed as being connected to the article. Please check the article itself for the full list of references which may differ. Not all references are currently linkable within the Digital Library.

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Hussain, J., Zafar, T., Fu, X., Ali, N., Chen, J., Frontalini, F., Hussain, J., Lina, X., Kontakiotis, G., and Koumoutsakou, O. (2024). Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach. Sci. Rep., 14.
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