  A semi-supervised model of peer review is introduced that is intended to
overcome the bias and incompleteness of traditional peer review. Traditional
approaches are reliant on human biases, while consensus decision-making is
constrained by sparse information. Here, the architecture for one potential
improvement (a semi-supervised, human-assisted classifier) to the traditional
approach will be introduced and evaluated. To evaluate the potential advantages
of such a system, hypothetical receiver operating characteristic (ROC) curves
for both approaches will be assessed. This will provide more specific
indications of how automation would be beneficial in the manuscript evaluation
process. In conclusion, the implications for such a system on measurements of
scientific impact and improving the quality of open submission repositories
will be discussed.
