Santos, Victor Silva dos 
; Gloaguen, Erwan; Louro, Vinicius Hector Abud ; Blouin, Martin
Source: Minerals. Unidade: IGC
Subjects: PROSPECÇÃO MINERAL, APRENDIZADO COMPUTACIONAL
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ABNT
SANTOS, Victor Silva dos 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, v. 12, n. 8, p. 941-, 2022Tradução . . Disponível em: https://doi.org/10.3390/min12080941. Acesso em: 09 maio 2024.APA
Santos, V. S. dos, Gloaguen, E., Louro, V. H. A., & 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, 12( 8), 941-. doi:10.3390/min12080941NLM
Santos VS dos, Gloaguen E, Louro VHA, Blouin M. 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 [Internet]. Minerals. 2022 ; 12( 8): 941-.[citado 2024 maio 09 ] Available from: https://doi.org/10.3390/min12080941Vancouver
Santos VS dos, Gloaguen E, Louro VHA, Blouin M. 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 [Internet]. Minerals. 2022 ; 12( 8): 941-.[citado 2024 maio 09 ] Available from: https://doi.org/10.3390/min12080941
