Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
A Survey of Generative Techniques for Spatial-Temporal Data Mining
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.09592.
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 2026-05-22T13:00:03.946769Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T13:01:33.985950Z
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 c744aa96-b8ee-4729-88fd-7d02253fdfd0 · inbound
FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation cites this paper.
FD-Bench: A Modular and Fair Benchmark for Data-driven Fluid Simulation A Survey of Generative Techniques for Spatial-Temporal Data Mining
Reference 133
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T13:01:33.991094Z
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 b4be7583-1d4d-4d60-aab2-9d52dbe18823 · inbound
UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration cites this paper.
UniMamba: A Unified Spatial-Temporal Modeling Framework with State-Space and Attention Integration A Survey of Generative Techniques for Spatial-Temporal Data Mining
Reference 4
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T14:51:08.474326Z
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 b588ae1f-0100-4609-b740-2b30059b02f6 · inbound
Efficient Prompt Learning for Traffic Forecasting cites this paper.
Efficient Prompt Learning for Traffic Forecasting A Survey of Generative Techniques for Spatial-Temporal Data Mining
Reference 52
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-12T08:06:26.739852Z
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.