Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2104.06297.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T14:18:54.376964Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T21:49:42.472322Z
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 efc031a1-f3df-4176-a489-b324af6328be · inbound
Machine learning for modelling unstructured grid data in computational physics: a review cites this paper.
Machine learning for modelling unstructured grid data in computational physics: a review Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations
Reference 81
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T21:49:42.478436Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.
Observation 71a04f3e-2079-42ad-99ef-2b66c889aec9 · inbound
Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates cites this paper.
Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations
Reference 49
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T14:18:54.376964Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.