Mining for Species, Locations, Habitats, and Ecosystems from Scientific Papers in Invasion Biology: A Large-Scale Exploratory Study with Large Language Models

dc.contributor.authorD'Souza, Jennifer
dc.contributor.authorLaubach, Zachary
dc.contributor.authorMustafa, Tarek Al
dc.contributor.authorZarrieß, Sina
dc.contributor.authorFrühstückl, Robert
dc.contributor.authorIllari, Phyllis
dc.contributor.editorBasile, Valerio
dc.contributor.editorBosco, Cristina
dc.contributor.editorGrasso, Francesca
dc.contributor.editorIbrahim, Muhammad Okky
dc.contributor.editorSkeppstedt, Maria
dc.contributor.editorStede, Manfred
dc.coverage.spatialTallinn, Estonia
dc.date.accessioned2025-02-17T11:38:20Z
dc.date.available2025-02-17T11:38:20Z
dc.date.issued2025-03
dc.description.abstractThis study explores the use of large language models (LLMs), specifically GPT-4o, to extract key ecological entities—species, locations, habitats, and ecosystems—from invasion biology literature. This information is critical for understanding species spread, predicting future invasions, and informing conservation efforts. Without domain-specific fine-tuning, we assess the potential and limitations of GPT-4o, out-of-the-box, for this task, highlighting the role of LLMs in advancing automated knowledge extraction for ecological research and management.
dc.identifier.isbn978-9908-53-114-4
dc.identifier.urihttps://hdl.handle.net/10062/107178
dc.language.isoen
dc.publisherUniversity of Tartu Library
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleMining for Species, Locations, Habitats, and Ecosystems from Scientific Papers in Invasion Biology: A Large-Scale Exploratory Study with Large Language Models
dc.typeArticle

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