Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Squared Wasserstein-2 Distance for Efficient Reconstruction of Stochastic Differential Equations
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.11354.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:42:57.996901Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T01:12:20.514165Z
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 a2c22e36-a41f-4508-9dd5-36d5af856fb3 · inbound
Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations cites this paper.
Reconstructing Noisy Gene Regulation Dynamics Using Extrinsic-Noise-Driven Neural Stochastic Differential Equations Squared Wasserstein-2 Distance for Efficient Reconstruction of Stochastic Differential Equations
Reference 40
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T01:12:20.516453Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.
Observation 482ecdc0-37b4-47a3-bc81-ae7f26a73cd7 · inbound
A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks cites this paper.
A generalized Wasserstein-2 distance approach for efficient reconstruction of random field models using stochastic neural networks Squared Wasserstein-2 Distance for Efficient Reconstruction of Stochastic Differential Equations
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:42:57.996901Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.