Development of an AI-driven Approach to Efficiently Implement Structured Literature Syntheses
This AI-driven tool ranks and synthesizes drought literature to accelerate science operations and applications.
We are developing an Artificial Intelligence (AI)‑driven workflow to rapidly synthesize thousands of papers, supporting literature reviews that summarize scientific findings and inform decision-making. An interdisciplinary team has been assembled to help test software and guide outcomes, ensuring accurate, reproducible outputs and making meta‑analysis more systematic. These efforts are being applied to ongoing work that is synthesizing drought and ecohydrology papers to evaluate responses in sagebrush landscapes of the western United States. The Actionable and Strategic Integrated Science and Technology (ASIST), Wyoming Landscape Conservation Initiative (WLCI), USGS scientists, land managers, and researchers developing literature syntheses will benefit from this workflow to improve meta-syntheses and enhance decision support for natural resource planning. By sharing lessons on AI integration, Quality Assurance/Quality Control (QA/QC), and workflow design, we will advance CDI’s goals for innovation, data integration, and community collaboration and provide practical guidance for integrating AI into USGS operations and applications.
This AI-driven tool ranks and synthesizes drought literature to accelerate science operations and applications.
We are developing an Artificial Intelligence (AI)‑driven workflow to rapidly synthesize thousands of papers, supporting literature reviews that summarize scientific findings and inform decision-making. An interdisciplinary team has been assembled to help test software and guide outcomes, ensuring accurate, reproducible outputs and making meta‑analysis more systematic. These efforts are being applied to ongoing work that is synthesizing drought and ecohydrology papers to evaluate responses in sagebrush landscapes of the western United States. The Actionable and Strategic Integrated Science and Technology (ASIST), Wyoming Landscape Conservation Initiative (WLCI), USGS scientists, land managers, and researchers developing literature syntheses will benefit from this workflow to improve meta-syntheses and enhance decision support for natural resource planning. By sharing lessons on AI integration, Quality Assurance/Quality Control (QA/QC), and workflow design, we will advance CDI’s goals for innovation, data integration, and community collaboration and provide practical guidance for integrating AI into USGS operations and applications.