Machine Learning Methods for Quantifying Uncertainty in Prospectivity Mapping of Magmatic-Hydrothermal Gold Deposits: A Case Study from Juruena Mineral Province, Northern Mato Grosso, Brazil.

MLA

Silva dos Santos, Victor, et al. “Machine Learning Methods for Quantifying Uncertainty in Prospectivity Mapping of Magmatic-Hydrothermal Gold Deposits: A Case Study from Juruena Mineral Province, Northern Mato Grosso, Brazil.” Minerals (2075-163X), vol. 12, no. 8, Aug. 2022, p. 941. EBSCOhost, https://doi.org/10.3390/min12080941.

APA

Silva dos Santos, V., Gloaguen, E., Hector Abud Louro, V., & Blouin, M. (2022). Machine Learning Methods for Quantifying Uncertainty in Prospectivity Mapping of Magmatic-Hydrothermal Gold Deposits: A Case Study from Juruena Mineral Province, Northern Mato Grosso, Brazil. Minerals (2075-163X), 12(8), 941. https://doi.org/10.3390/min12080941

Chicago

Silva dos Santos, Victor, Erwan Gloaguen, Vinicius Hector Abud Louro, and Martin Blouin. 2022. “Machine Learning Methods for Quantifying Uncertainty in Prospectivity Mapping of Magmatic-Hydrothermal Gold Deposits: A Case Study from Juruena Mineral Province, Northern Mato Grosso, Brazil.” Minerals (2075-163X) 12 (8): 941. doi:10.3390/min12080941.