<p>Physics-informed data augmentations have been used to improve data analytic models where attributed calibration data are limited, a common scenario in nuclear nonproliferation. This study used a small dataset of labeled gamma-ray spectra spanning several classes of shielded radiological material transfers collected at a real multiuse nuclear facility. Augmentations are used to increase the number of labeled data available for training supervised models for classifying transfer types. When trained with augmented data, relatively low-capacity models can achieve benchmark test performance with severely limited initial labeled data. This work motivates developing comparable augmentations for other measurement modalities relevant to nuclear nonproliferation.</p>