The paper describes a potential platform to facilitate academic peer review
with emphasis on early-stage research. This platform aims to make peer review
more accurate and timely by rewarding reviewers on the basis of peer prediction
algorithms. The algorithm uses a variation of Peer Truth Serum for
Crowdsourcing (Radanovic et al., 2016) with human raters competing against a
machine learning benchmark. We explain how our approach addresses two large
productive inefficiencies in science: mismatch between research questions and
publication bias. Better peer review for early research creates additional
incentives for sharing it, which simplifies matching ideas to teams and makes
negative results and p-hacking more visible