I'm familiar with bootstrapping--both the classical bootstrap, which fits the model repeatedly on different simulated datasets to evaluate model uncertainty, an

I'm familiar with bootstrapping--both the classical bootstrap, which fits the model repeatedly on different simulated datasets to evaluate model uncertainty, and bagging, which fits the model repeatedly on different simulated datasets as inputs for ensemble learning. Neither of those is what was done here, which was fitting the model once on a greatly inflated synthetic sample.