Precision and recall are useful indices to evaluate an operation, algorithm, system, and other subjects from two different facets. However, they are not readily available when the subject is still in progress because the truth set, which is required to calculate recall, is unknown. In this study, a method to predict the size of the truth set of an inquiry still in progress is presented, which consists of a classical 18th century mechanics found and formulated by Isaac Newton, today known as “Newton’s cooling law”, with some set-theoretical trick and executed by Markov Chain Monte Carlo. The developed method is applied to nation-wide scale collections of identifications of the authors of academic articles as the affiliation data of Japanese national research organizations, and obtain recall values, as a part of objective, evidence-based policy for science and technology of the Japanese government. The author identification result is naturally represented as a bipartite directed graph, from the set of authors to the set of affiliation data. We conduct a sort of network prediction, not on the bipartite graph itself but on its vertices size and obtain the true graph size by using a simple and straightforward probabilistic model, which is implemented by also a classical, yet recently developing probabilistic inference method.