Geologically Constrained Probabilistic Mapping of Gold Potential, Baguio District, Philippines

By Emmanuel John Muico Carranza, M. Hale. 2000

105 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Geochemistry and Petrology

View Paper: https://www.semanticscholar.org/paper/4aa25fef8f62e2bd953a74756434190d0d60aad2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Alice G. Laborte
2. Emmanuel John Muico Carranza
3. Renguang Zuo

In Environmental Engineering

1. Alice G. Laborte
2. Emmanuel John Muico Carranza

Citations in the corpus

• Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines) (2015, background): “In addition, Carranza and Laborte (2014) found that, in data-driven predictive mapping of gold prospectivity in the Baguio district (Philippines) based on 19 training locations for epithermal Au deposits, RF is not sensitive to different sets of non-deposit locations used for training together with ”