Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction
As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2310.03030.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T12:03:33.240298Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z
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 8b12f4c5-9b37-4ac2-a1ff-36b536a1dbbb · inbound
From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines cites this paper.
From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction
Reference 95
- Resolution
- verified exact
- arxiv_id, observed 2026-06-27T17:31:07.495233Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 723fbc5a-e6e4-44cb-bedb-cd354d0c6171 · inbound
From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines cites this paper.
From Text to Discovery: How Large Language Models Are Reshaping Research Across Scientific and Humanistic Disciplines GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction
Reference 95
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T12:03:33.240298Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 417c75a3-1af3-4a5f-9405-b61b2e209227 · inbound
SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing cites this paper.
SCTA: An Agentic Framework for Stable and Interpretable Target Gene Discovery from Single-Cell RNA Sequencing GPT-MolBERTa: GPT Molecular Features Language Model for molecular property prediction
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-07-30T11:26:39.128025Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.