Source: Journal of Materials Research and Technology. Unidade: EP
Subjects: ANÁLISE DE DADOS, MINERALOGIA APLICADA, MINÉRIOS, OURO
ABNT
COSTA, Fabrizzio Rodrigues e CARNEIRO, Cleyton de Carvalho e ULSEN, Carina. Predicting gold accessibility from mineralogical characterization using machine learning algorithms. Journal of Materials Research and Technology, v. 29, p. 10 , 2024Tradução . . Disponível em: https://doi.org/10.1016/j.jmrt.2024.01.139. Acesso em: 31 out. 2024.APA
Costa, F. R., Carneiro, C. de C., & Ulsen, C. (2024). Predicting gold accessibility from mineralogical characterization using machine learning algorithms. Journal of Materials Research and Technology, 29, 10 . doi:10.1016/j.jmrt.2024.01.139NLM
Costa FR, Carneiro C de C, Ulsen C. Predicting gold accessibility from mineralogical characterization using machine learning algorithms [Internet]. Journal of Materials Research and Technology. 2024 ;29 10 .[citado 2024 out. 31 ] Available from: https://doi.org/10.1016/j.jmrt.2024.01.139Vancouver
Costa FR, Carneiro C de C, Ulsen C. Predicting gold accessibility from mineralogical characterization using machine learning algorithms [Internet]. Journal of Materials Research and Technology. 2024 ;29 10 .[citado 2024 out. 31 ] Available from: https://doi.org/10.1016/j.jmrt.2024.01.139