bioRxivMicrobiologyPreprint
Introducing Large Language Models to Human-Based Etymological Classification in Zooplankton
Haruto SugenoiD, Keito InoshitaiD, nojiri kota, Takumi TagaiD
Abstract
Short AbstractWe benchmarked LLM-based against expert epithet-etymology labelling for 989 zooplankton species, finding high concordance except in culturally nuanced names, confirming LLMs value as rapid screening aids.
- Citations
- 2
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Automated Labeling of Scientific Names and Etymological Trend Analysis in Phytophagous Arthropods Using Large Language Model
Kota Nojiri, Keito Inoshita, Haruto Sugeno·ZOOLOGICAL SCIENCEPeer-reviewed·Sep 4, 2025
- Large language models possess some ecologicalknowledge, but how much?
Filip Dorm, Joseph W. Millard, … Oisin Mac Aodha·Ecological InformaticsPeer-reviewed·Mar 17, 2026
- Large language models help facilitate the automated synthesis of information on potential pest controllers
D. Scheepens, J. Millard, … T. Newbold·bioRxiv·Jan 12, 2024
- Utilizing large language models to construct a dataset of Württemberg's 19th-century fauna from historical records
Maximilian Teich, Belen Escobari, Malte Rehbein·bioRxiv·Oct 14, 2025
- Enhanced semantic classification of microbiome sample origins using Large Language Models (LLMs)
Daniela Gaio, Janko Tackmann, … Christian von Mering·GigaSciencePeer-reviewed·Jan 1, 2026
- AI-Driven Knowledge Synthesis for Food WebParameterisation
Scott Spillias, Elizabeth A. Fulton, … Rowan Trebilco·Environmental Modelling & SoftwarePeer-reviewed·Jan 7, 2026