Publisher Correction: Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain
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Nature Portfolio
2021
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oai:doaj.org-article:34e7d0a03dd448b38c3eb0fb0611bfeb2021-11-28T12:15:46ZPublisher Correction: Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain10.1038/s41598-021-02402-z2045-2322https://doaj.org/article/34e7d0a03dd448b38c3eb0fb0611bfeb2021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02402-zhttps://doaj.org/toc/2045-2322Tom ElliotRobert MorseDuane SmytheAshley NorrisNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-1 (2021) |
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Medicine R Science Q Tom Elliot Robert Morse Duane Smythe Ashley Norris Publisher Correction: Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain |
format |
article |
author |
Tom Elliot Robert Morse Duane Smythe Ashley Norris |
author_facet |
Tom Elliot Robert Morse Duane Smythe Ashley Norris |
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Tom Elliot |
title |
Publisher Correction: Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain |
title_short |
Publisher Correction: Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain |
title_full |
Publisher Correction: Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain |
title_fullStr |
Publisher Correction: Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain |
title_full_unstemmed |
Publisher Correction: Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain |
title_sort |
publisher correction: evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in britain |
publisher |
Nature Portfolio |
publishDate |
2021 |
url |
https://doaj.org/article/34e7d0a03dd448b38c3eb0fb0611bfeb |
work_keys_str_mv |
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1718408069756485632 |