Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2312.09926.
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 2026-08-12T14:19:21.390766Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
4
pith, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation 5b619cb9-d9ad-4e26-b81e-f75d8c55b60b · inbound
Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization cites this paper.
Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T14:19:21.390766Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 983b0cd6-864c-4f4c-a8f9-e58585c205cf · inbound
AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales cites this paper.
AIFS-SUBS: Extending Data-Driven Forecasting to Sub-Seasonal Timescales FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models
Reference 7
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-11T08:57:58.300874Z
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.