A research team is developing a new model. Their initial, 'weak' model achieves an accuracy of 50% on a specific classification task. They have a more powerful, 'strong' model architecture which, if trained on a perfect dataset, could theoretically reach a 'ceiling' accuracy of 90%. To save on labeling costs, they use the weak model to supervise the training of the strong model. After this process, the newly trained strong model achieves an accuracy of 80%. Based on this scenario, calculate the Performance Gap Recovered (PGR).