Review Invitations

From

Dr. Sarah Johnson, Associate Editor

s.johnson@nobleblocks.org

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Abstract

This paper proposes a novel approach to scientific peer review using blockchain technology to increase transparency, reduce bias, and provide appropriate incentives for reviewers. We present a prototype system and preliminary results from a pilot study involving 50 manuscripts and 120 reviewers across multiple disciplines.

From

Dr. Michael Chen, Section Editor

m.chen@nobleblocks.org

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Abstract

We develop and evaluate several machine learning models to predict the outcome of peer review based on manuscript features, reviewer characteristics, and historical data. Our best-performing model achieves 78% accuracy in predicting whether a manuscript will be accepted, rejected, or require major revisions.

From

Dr. Emily Rodriguez, Associate Editor

e.rodriguez@nobleblocks.org

Invitation Date

Review Due Date

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Abstract

This study examines how open peer review affects the quality, thoroughness, and civility of reviewer comments. Using a randomized controlled trial with 200 manuscripts, we compare traditional anonymous review with open review where reviewer identities are disclosed to authors and readers.