For the active learning approach, the positive label ratio quickly reaches, after an initial sharp peak, a temporary equilibrium just below the threshold value of 0.3. This equilibrium lasts for approximately 4000 iterations before it starts to decline gradually toward a value of 0.1. In contrast, the random selection approach maintains, again after an initial sharp peak, a positive ratio of around 0.11. These observations show that the data efficiency and performance advantage of the active learning approach is not based solely on data balancing.