We keep in mind that the gain scores scale to different sizes of datasets: if the model had a gain of x on the third decile of the test set, we can extended our annotation to cover the third decile of the project data as ordered by the model, we would be likely to have a similar gain on our larger dataset and ultimate linguistic resource.
just like with gain, we can extrapolate from the cost calculations on our test set to estimate the cost of annotating a larger-scale unlabeled dataset.