  References, the mechanism scientists rely on to signal previous knowledge,
lately have turned into widely used and misused measures of scientific impact.
Yet, when a discovery becomes common knowledge, citations suffer from
obliteration by incorporation. This leads to the concept of hidden citation,
representing a clear textual credit to a discovery without a reference to the
publication embodying it. Here, we rely on unsupervised interpretable machine
learning applied to the full text of each paper to systematically identify
hidden citations. We find that for influential discoveries hidden citations
outnumber citation counts, emerging regardless of publishing venue and
discipline. We show that the prevalence of hidden citations is not driven by
citation counts, but rather by the degree of the discourse on the topic within
the text of the manuscripts, indicating that the more discussed is a discovery,
the less visible it is to standard bibliometric analysis. Hidden citations
indicate that bibliometric measures offer a limited perspective on quantifying
the true impact of a discovery, raising the need to extract knowledge from the
full text of the scientific corpus.
