Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
On a novel approach for modeling liquid crystalline flows
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2003.12504.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T16:48:57.212131Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T16:49:06.434231Z
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 7dbbfe8e-8cb4-48e9-ae4a-56a3feb770e0 · inbound
NLI4VolVis: Natural Language Interaction for Volume Visualization via LLM Multi-Agents and Editable 3D Gaussian Splatting cites this paper.
NLI4VolVis: Natural Language Interaction for Volume Visualization via LLM Multi-Agents and Editable 3D Gaussian Splatting On a novel approach for modeling liquid crystalline flows
Reference 2003
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T16:49:06.514689Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 2a8aba14-850d-41ff-8b3f-20ad4be5fc97 · inbound
WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks cites this paper.
WarpPINN-fibers: improved cardiac strain estimation from cine-MR with physics-informed neural networks On a novel approach for modeling liquid crystalline flows
Reference 2003
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T20:22:55.298661Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.