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Spatio-temporal copper prospectivity in the American Cordillera predicted by positive-unlabelled machine learning – supplement

descriptionPublicationkeyboard_double_arrow_right Article 19 Jul 2023 English Publisher:Zenodo
Authors: Alfonso, Christopher P.; Müller, R. Dietmar; Mather, Ben; Anthony, Michele;

doi: 10.5281/zenodo.8207167 , 10.5281/zenodo.8157691 , 10.5281/zenodo.8404597 , 10.5281/zenodo.13777155 , 10.5281/zenodo.8157690 , 10.5281/zenodo.11239167 , 10.5281/zenodo.11140991 , 10.5281/zenodo.8333702

Spatio-temporal copper prospectivity in the American Cordillera predicted by positive-unlabelled machine learning – supplement

Abstract

Source data used to create training and test datasets for the machine learning models presented in the paper: Alfonso, C.P., Müller, R.D., Mather, B., Anthony, M. In prep. Spatio-temporal copper prospectivity in the American Cordillera predicted by positive-unlabelled machine learning. Python notebooks and scripts can be found in the GitHub repository.

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Keywords

porphyry copper, machine learning, Cordillera

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