Reproducibility of scientific computation is a critical factor in validating its underlying process, but it is often elusive. Complexity and continuous evolution in software systems have introduced new challenges for reproducibility across a myriad of computational sciences, resulting in growing debt. This requires a comprehensive domain-agnostic study to define and assess Reproducibility Debt in scientific software, thus uncovering and classifying all underlying factors attributed towards its emergence and identification i.e., causes and effects. Moreover, an organized map of prevention strategies is imperative to guide researchers for its proactive management.