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Paper Citation Record · LEDGER

HydroNets: Leveraging River Structure for Hydrologic Modeling

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2007.00595.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2007.00595 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:11:12.070418Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-11T10:33:44.137224Z

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 579cb9b6-0127-406d-9be0-1746657ecedd · inbound

Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks cites this paper.

Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks HydroNets: Leveraging River Structure for Hydrologic Modeling

Reference 38

Resolution
verified exact
local_arxiv, observed 2026-08-11T10:33:44.141699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=arxiv_source observed=2026-08-11T10:33:43.970821Z digest=sha256:458714ad36eec5bb31d31b6784e6156b326ad505adef34cb8c71f516f22302ca

Observation b31ebd4d-0fd9-4a61-a1d1-2a015f923d22 · inbound

Multi-Scale Graph Learning for Anti-Sparse Downscaling cites this paper.

Multi-Scale Graph Learning for Anti-Sparse Downscaling HydroNets: Leveraging River Structure for Hydrologic Modeling

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-16T04:11:12.070418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-16T04:11:12.070418Z digest=sha256:f27cac7ccfcc24bfa423b15a097c29c0caeb9c2250a4f0e7d679ebd0248974ca