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Wikipedia is a widely used online reference work which cites hundreds of thousands of scientific articles across its entries. The quality of these citations has not been previously measured, and such measurements have a bearing on the reliability and quality of the scientific portions of this reference work. Using a novel technique, a massive database of qualitatively described citations, and machine learning algorithms, we analyzed 1 923 575 Wikipedia articles which cited a total of 824 298 scientific articles in our database and found that most scientific articles cited by Wikipedia articles are uncited or untested by subsequent studies, and the remainder show a wide variability in contradicting or supporting evidence. Additionally, we analyzed 51 804 643 scientific articles from journals indexed in the Web of Science and found that similarly most were uncited or untested by subsequent studies, while the remainder show a wide variability in contradicting or supporting evidence. [ABSTRACT FROM AUTHOR]

Key points: While the importance of citation context has long been recognized, simple citation counts remain as a crude measure of importance.Providing citation context should support the publication of careful science instead of headline‐grabbing and salami‐sliced non‐replicable studies.Machine learning has enabled the extraction of citation context for the first time, and made the classification of citation types at scale possible. [ABSTRACT FROM AUTHOR]