Include a concise model summary that describes inputs, outputs, and key assumptions so readers can understand how the model operates before diving into equations. A short flow diagram or schematic in the manuscript can improve comprehension and review quality.

Provide details on computational resources, solver settings, and runtime considerations when they affect model performance or scalability. Reporting these elements helps reviewers assess feasibility and supports reproducibility across different research environments.

Benchmark new algorithms against established baselines and explain evaluation criteria, including accuracy, stability, and computational efficiency. Clear benchmarking strengthens the contribution and clarifies why the proposed method advances the field.

Describe how uncertainty was quantified and how sensitivity analyses informed conclusions. If uncertainty was not evaluated, explain why and clarify the limitations this introduces for decision making or predictive accuracy.

Explain the decision context for applied models, such as engineering design, policy analysis, or operational planning. Reviewers look for a clear link between model outputs and real world use cases.

Document preprocessing steps for input data, including normalization, filtering, or feature engineering, to ensure transparency and enable independent replication of results.

Define notation and acronyms consistently across the manuscript, especially when multiple models or scenarios are compared. Consistency reduces ambiguity during peer review.

Discuss limitations candidly, including boundary conditions, assumptions that may not generalize, and potential sources of bias in data or modeling choices.