Paper summary generated by OpenAI: This study presents a machine learning-based evidence map that characterizes the landscape of ocean-related options (OROs) for climate change mitigation and adaptation. By training a large language model, the research analyzes 44,193 articles, revealing a significant focus on mitigation strategies (80%) while highlighting critical research gaps in under-explored ecosystems and the need for integrated assessments of diverse ORO types. Additionally, the findings expose social inequalities linked to the uneven distribution of research efforts relative to global climate responsibilities and risks. These insights are crucial for optimizing the effectiveness of OROs and guiding future research priorities in the context of climate action.
