Rachel H. Parkinson, Henry Cerbone, Mikael Mieskolainen, Shuxiang Cao, Alex Wilson, Sergio Albacete, Emily B. Armstrong, Chris Bass, Cristina Botías, Andrew Brown, Angela J. Hayward, Lina Herbertsson, Andrew K. Jones, Nicolas Nagloo, Elizabeth Nicholls, Elisa Rigosi, Fabio Sgolastra, Harry Siviter, Dara A. Stanley, Lars Straub, Edward A. Straw, Rafaela Tadei, Kieran Walter, H. F. Stevance, Ryan K. Daniels, Ben Lambert, Stephen J. Roberts
The volume and complexity of scientific literature are expanding rapidly, making it increasingly difficult to extract and synthesise information across studies. This challenge is particularly acute in the biological sciences, where evidence spans multiple levels of organisation and heterogeneous experimental designs. Large Language Model (LLM) pipelines offer a scalable route to evidence synthesis, but many existing approaches lack transparency, modularity, and effective mechanisms for human oversight. We present MetaBeeAI, an open-source, modular pipeline that integrates established LLM techniques into a coherent, auditable workflow for structured data extraction in biology. MetaBeeAI combines modular prompting, multi-pass extraction, and expert-in-the-loop validation within an interface that presents model outputs alongside source text, enabling inspection, correction, and iterative refinement. The pipeline produces machine-readable records of prompts, configurations, and expert annotations, supporting reproducibility and continuous improvement. We apply MetaBeeAI to 924 research papers on bees and pesticides, extracting structured information on species, compounds, exposure designs, and experimental context. Evaluation demonstrates improved consistency, convergence with expert judgement, and robustness across heterogeneous biological studies, highlighting the value of expert-guided refinement. MetaBeeAI provides a transparent and extensible framework for scalable evidence synthesis, supporting reliable integration of LLMs into biological research workflows.