Arthropod Pesticide Resistance Scholar: A Large Language Model-Assisted Literature-Mining Framework for Building an Evidence-Grade Arthropod Pesticide Resistance Knowledge Base

The rapid evolution of insecticide and acaricide resistance in arthropod pests threatens crop protection and sustainable pest management, while the supporting evidence is dispersed across tens of thousands of papers. Existing curated databases are manually maintained and taxon-narrow, and they rarely link case-level monitoring with mechanistic evidence. Here, we developed Arthropod Pesticide Resistance Scholar (APRS), a large language model-assisted extraction framework that progressively extracts resistance evidence section by section. APRS confirms species, pesticide, and gene or population relationships step by step and retains only records with quantitative or experimental support, generating two evidence tracks: monitoring (species, pesticide, population, and resistance phenotype) and mechanism (species, pesticide, gene, and supporting method). The released knowledge base contains 40,950 structured cases from 4094 publications, covering 359 species, 313 pesticides, and 2563 genes, with resistance evidence spanning 1936 to 2025. Against a fixed 100-paper expert gold set comprising 696 manually curated cases, case-level extraction achieved precision, recall, and F1 scores of 0.94, 0.91, and 0.93, respectively. APRS provides structured filtering and a case-level retrieval-augmented assistant, supporting resistance monitoring, mechanism discovery, resistance management, and integrated pest management.