Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2304.02213.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T10:16:54.517386Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T12:29:52.203444Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation a849bf6c-2d6d-472d-b8e7-ab4a2ba4e39f · inbound
Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks cites this paper.
Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT
Reference 39
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:16:54.517386Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7cd023a-c400-4af5-9888-885ea312ca45 · inbound
Reaction-Network-Level Discovery of Ammonia Synthesis Catalysts via Ten-Million-Scale Generative Exploration cites this paper.
Reaction-Network-Level Discovery of Ammonia Synthesis Catalysts via Ten-Million-Scale Generative Exploration Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T12:29:52.205076Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation afefe37b-b0e1-4f13-bd17-a32651893d31 · inbound
Reaction-Network-Level Discovery of Ammonia Synthesis Catalysts via Ten-Million-Scale Generative Exploration cites this paper.
Reaction-Network-Level Discovery of Ammonia Synthesis Catalysts via Ten-Million-Scale Generative Exploration Large Language Models as Master Key: Unlocking the Secrets of Materials Science with GPT
Reference 27
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T12:47:53.699751Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.