An LLM-based pipeline to semi-automate the assessment of Invasive Alien Species impacts, using the DataM module "GPT Scheduler"
Summary
Effective management of invasive alien species (IAS) requires a cross-sectoral approach and identification of indirect "cascade" effects on multiple sectors.
To address the challenge of evaluating IAS impacts, we explored the potential of artificial intelligence (AI) to support automatic literature reviews.
Using AI, we analysed 498 IAS impacting 27 policy domains and demonstrated the potential for semi-automation to enhance efficiency, the importance of expert involvement, and the value of AI in extracting contextualized information.
Ultimately this research aims to develop a scientific, generalisable method for routine operationalisation.
This study is described in the JRC Technical Report: GTP Scheduler: AI to support JRC work on Alien Species.
GPT Scheduler
The GPT Scheduler enables users to automatically retrieve information from Large Language Models (LLM) in a structured and organized manner. The extracted data is then stored in a data model within Qlik Sense, where it can be easily analysed, visualized, and leveraged using Qlik Sense's business intelligence capabilities.
It contains three modules:
- Qlik Sense
- DataM tool: Data-Modelling platform of resource of economics
- GPT@JRC
A detailed description of this tool can be found in the GPT Scheduler - JRC Technical Report.
