  Deep learning still struggles with certain kinds of scientific data. Notably,
pretraining data may not provide coverage of relevant distribution shifts
(e.g., shifts induced via the use of different measurement instruments). We
consider deep learning models trained to classify the synthesis conditions of
uranium ore concentrates (UOCs) and show that model editing is particularly
effective for improving generalization to distribution shifts common in this
domain. In particular, model editing outperforms finetuning on two curated
datasets comprising of micrographs taken of U$_{3}$O$_{8}$ aged in humidity
chambers and micrographs acquired with different scanning electron microscopes,
respectively.
