A Coding Protocol for Labeling Scientific Literature on Carbon Dioxide Removal to Train Machine Learning Models v1

Carbon dioxide removal (CDR) plays an important role in any strategy to limit global warming to well below 2°C. As research on CDR rapidly expands, keeping pace with the scientific evidence through rigorous and transparent evidence synthesis is essential for informing sustainable deployment. To support this, we use artificial intelligence (AI) to develop the first comprehensive systematic map of CDR research. A critical first step in this process is the creation of a high-quality training dataset to enable machine learning models to accurately identify relevant scientific literature. This coding protocol provides a structured methodology for generating such a dataset, including the development of a coding scheme, inclusion criteria, and annotation workflow. The protocol ensures consistency and reproducibility, facilitating scalable AI-assisted evidence mapping in the CDR domain.