  While ancient scientists often had patrons to fund their work, peer review of
proposals for the allocation of resources is a foundation of modern science. A
very common method is that proposals are evaluated by a small panel of experts
(due to logistics and funding limitations) nominated by the grant-giving
institutions. The expert panel process introduces several issues - most
notably: 1) biases introduced in the selection of the panel. 2) experts have to
read a very large number of proposals. Distributed Peer Review promises to
alleviate several of the described problems by distributing the task of
reviewing among the proposers. Each proposer is given a limited number of
proposals to review and rank. We present the result of an experiment running a
machine-learning enhanced distributed peer review process for allocation of
telescope time at the European Southern Observatory. In this work, we show that
the distributed peer review is statistically the same as a `traditional' panel,
that our machine learning algorithm can predict expertise of reviewers with a
high success rate, and we find that seniority and reviewer expertise have an
influence on review quality. The general experience has been overwhelmingly
praised from the participating community (using an anonymous feedback
mechanism).
