Work

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

Year: 2023

Type: article

Abstract: Source data and code 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 also be found in the GitHub repository.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.8157690

Authors: Alfonso, Christopher P., Müller, R. Dietmar, Mather, Ben, Anthony, Michele

Institutions: The University of Sydney, BHP (Australia)

Language: English

Topic: Geochemistry and Geologic Mapping

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6894050522

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