Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learn molecular representations from large-scale unlabeled molecules for drug discovery
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2012.11175.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T10:38:06.971755Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T23:50:54.400489Z
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 1ecc842f-5fba-4f5e-a618-a044c0990b04 · inbound
Self-Supervised Learning for Graph-Structured Data in Healthcare Applications: A Comprehensive Review cites this paper.
Self-Supervised Learning for Graph-Structured Data in Healthcare Applications: A Comprehensive Review Learn molecular representations from large-scale unlabeled molecules for drug discovery
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T10:38:06.971755Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aa1c0682-668a-46f8-8b06-dafd982089a8 · inbound
BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning cites this paper.
BiScale-GTR: Fragment-Aware Graph Transformers for Multi-Scale Molecular Representation Learning Learn molecular representations from large-scale unlabeled molecules for drug discovery
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T23:50:54.403588Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.