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Moreno, W. Emilio G.; Leães, Áttila; Bassani, Marcel Antonio Arcari; Marques, Diego; Costa, João Felipe Coimbra Leite (2025) Machine Learning Regressors: An Alternative to Compact Grades Information, Generate Secondary Information, and Improve the Density Block Models. Mining, Metallurgy & Exploration, 42 (5). doi:10.1007/s42461-025-01335-9

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Reference TypeJournal (article/letter/editorial)
TitleMachine Learning Regressors: An Alternative to Compact Grades Information, Generate Secondary Information, and Improve the Density Block Models
JournalMining, Metallurgy & Exploration
AuthorsMoreno, W. Emilio G.Author
Leães, ÁttilaAuthor
Bassani, Marcel Antonio ArcariAuthor
Marques, DiegoAuthor
Costa, João Felipe Coimbra LeiteAuthor
Year2025 (October)Volume42
Issue5
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1007/s42461-025-01335-9Search in ResearchGate
Generate Citation Formats
Mindat Ref. ID19088000Long-form Identifiermindat:1:5:19088000:4
GUID0
Full ReferenceMoreno, W. Emilio G.; Leães, Áttila; Bassani, Marcel Antonio Arcari; Marques, Diego; Costa, João Felipe Coimbra Leite (2025) Machine Learning Regressors: An Alternative to Compact Grades Information, Generate Secondary Information, and Improve the Density Block Models. Mining, Metallurgy & Exploration, 42 (5). doi:10.1007/s42461-025-01335-9
Plain TextMoreno, W. Emilio G.; Leães, Áttila; Bassani, Marcel Antonio Arcari; Marques, Diego; Costa, João Felipe Coimbra Leite (2025) Machine Learning Regressors: An Alternative to Compact Grades Information, Generate Secondary Information, and Improve the Density Block Models. Mining, Metallurgy & Exploration, 42 (5). doi:10.1007/s42461-025-01335-9
In(2025, October) Mining, Metallurgy & Exploration Vol. 42 (5). Springer Science and Business Media LLC

References Listed

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