Making an ensemble model for high F1 score
In general this would mean that P predicts only a small number of instances higher than 0.9 whereas R predicts most instances higher than 0.9. Therefore a weighted average of the two scores will fall somewhere in the middle, likely resulting in a moderate precision and moderate recall.
This can give significantly better results but only if the two classifiers are complementary, i.e. they predict instances in a way different enough from each other. Otherwise it's equivalent to tuning the threshold on a single classifier.