This short paper highlights the growing importance of information retrieval
(IR) engines in the scientific community, addressing the inefficiency of
traditional keyword-based search engines due to the rising volume of
publications. The proposed solution involves structured records, underpinning
advanced information technology (IT) tools, including visualization dashboards,
to revolutionize how researchers access and filter articles, replacing the
traditional text-heavy approach. This vision is exemplified through a proof of
concept centered on the ``reproductive number estimate of infectious diseases''
research theme, using a fine-tuned large language model (LLM) to automate the
creation of structured records to populate a backend database that now goes
beyond keywords. The result is a next-generation IR method accessible at
this https URL

这篇论文强调信息检索引擎在科学界日益重要，针对传统基于关键词的搜索引擎由于文章数量不断增多而效率低下的问题提出了解决方案，通过构建结构化记录和使用先进信息技术工具（包括可视化仪表盘），彻底改变研究人员访问和筛选文章的传统文本密集型方法。通过以 “传染病的再生数估计” 研究主题为核心的概念验证，使用调试过的大型语言模型自动创建结构化记录并填充后端数据库，以取代关键词。结果是一种新一代的信息检索方法，可通过此 https 网址访问。