  With the start of a new Great Observatories era, there is renewed concern
that the demand for these forefront facilities, through proposal pressure, will
exceed conventional peer-review management's capacity for ensuring an unbiased
and efficient selection. There is need for new methods, strategies, and tools
to facilitate those reviews. Here, we describe PACMan2, an updated tool for
proposal review management that utilizes machine learning models and techniques
to topically categorize proposals and reviewers, to match proposals to
reviewers, and to facilitate proposal assignments, mitigating some conflicts of
interest. We find that the classifier has cross-validation accuracy of
$80.0\pm2.2\%$ on proposals for time on the Hubble Space Telescope and the
James Webb Space Telescope.
