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

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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-09-18
DOI
https://doi.org/10.5281/zenodo.13777155
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
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article

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

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

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

article en

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.

Zenodo (CERN European Organization for Nuclear Research)
The University of Sydney (AU), BHP Laboratories (IE)
Openalex Percentile: Top 57%
Geochemistry and Geologic Mapping
DOIOpen Access PDFOpenAlex
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Spatio-temporal copper prospectivity in the American Cordillera predicted by positive-unlabelled machine learning – supplement · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS