Overview of our determination of the probability to identify giants in the LoTSS DR2 with above-noise surface brightnesses, as a function of projected length and redshift – through Radio Galaxy Zoo: LOFAR and our machine learning pipeline (top row), through the search of Oei et al. (2023a, middle row), and through these methods in unison (bottom row). Each of the upper four panels shows a binary logistic regression following the theory of Sect. 2.4.2 and the practical considerations of Sect. 4.9.1. The left column shows results from all available data, whilst the right column shows results from rebalanced data. In our Bayesian inference, we used the latter results.