  It is desirable to coarsely classify short scientific texts, such as grant or
publication abstracts, for strategic insight or research portfolio management.
These texts efficiently transmit dense information to experts possessing a rich
body of knowledge to aid interpretation. Yet this task is remarkably difficult
to automate because of brevity and the absence of context. To address this gap,
we have developed a novel approach to generate and appropriately assign coarse
domain-specific labels. We show that a Large Language Model (LLM) can provide
metadata essential to the task, in a process akin to the augmentation of
supplemental knowledge representing human intuition, and propose a workflow. As
a pilot study, we use a corpus of award abstracts from the National Aeronautics
and Space Administration (NASA). We develop new assessment tools in concert
with established performance metrics.
