The replicability of research is crucial for building trust in the peer review process and transitioning knowledge to realworld applications. While manual peer review excels in some regards, the variability of reviewer expertise, publication requirements, and research domains brings about uncertainty in the process. Replicability, in particular, is not necessarily a priority; this is evidenced by repeated failures in replication attempts such as the Psychology Reproducibility Project, where 61 of 100 replications fail. Improving human comprehension of decisive factors is crucial for integrating automated systems for replicability prediction into the review process. We develop a robust, automated method for semantic parsing, information extraction, and replication prediction that operates directly on PDFs. We introduce features that have not been explored in prior work, construct argument structures to guide understanding, and provide preliminary results for replication prediction.