Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-11T18:05:58.982877Z
Paper Citation Record · LEDGER
As of 12 August 2026, this Paper Citation Record lists 100 of 115 outbound references and 2 inbound Pith citation observations for arXiv:2412.08258.
A citation records a reference. It does not transfer a finding from one paper to another.
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Source: paper_references, paper_reference_links, observed 2026-08-11T18:05:58.982877Z
One-hop event checks from named stored sources.
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Pith citing papers itemized under the disclosed page cap.
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A source-named dated measurement, never combined with another source.
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100 of 115 outbound references displayed
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Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field Salatino, T
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Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field Hedden, Taxonomies and controlled vocabularies best practices for metadata, Journal of Digital Asset Man- agement 6 (2010) 279–284
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Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field Zaharee, Building controlled vocabularies for metadata harmonization, Bulletin of the American Society for Information Science and Technology 39 (2) (2013) 39–42
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Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field 3759, CEUR, 2024
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Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field Unresolved cited work
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Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field Unresolved cited work
Reference 97
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Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field Templeton, T
Reference 98
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Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field ,→ ,→ Given the previous definitions, determine which one of the following statements is correct:,→
Reference 102
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Agentic Retrieval of Topics and Insights from Earnings Calls Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field
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Automatic Ontology Construction Using LLMs as an External Layer of Memory, Verification, and Planning for Hybrid Intelligent Systems Large Language Models for Scholarly Ontology Generation: An Extensive Analysis in the Engineering Field
Reference 15
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