Accelerating scientific discovery through AI-driven literature synthesis and meta-analysis using large language models [2024 INV#WO0000000201793]]
Description
A team of USGS researchers are conducting a review of literature on drought and its affects on the western United States. Due to a large volume of literature, we are using AI to facilitate an efficient and reliable literature synthesis that can inform decision-making. These include effectively ranking most relevant literature to questions, identifying gaps in the literature, identifying data gaps, and coalescing currently known information.
Detailed example
The outputs from AI will include identifications of study areas, relatedness to multiple topics (drought characteristics, hydrological and ecological processes, and hydrological and ecological responses). Using multiple LLMs and deep learning methods to evaluate the same context, we will have comparative information for model ensembles.
AI / analytics pattern
Natural Language Processing: AI that processes, interprets, and shares information in human language.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
These efforts will help expedite reviewing thousands of scientific studies that can support the project, but also support future efforts of compiling relevant information for NEPA planning or related tasks.
Controls / human review
ATO: No; PIA: Not published