  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
https://orkg.org/usecases/r0-estimates.
