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Paper Citation Record · LEDGER

An Efficient Modern Baseline for FloodNet VQA

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2205.15025.

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

pith.paper-citation-record.v1
2205.15025 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 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:29:51.760963Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T15:42:22.438253Z

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 7d1647bc-da45-43ca-82a3-f5460f787bf3 · inbound

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning cites this paper.

Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning An Efficient Modern Baseline for FloodNet VQA

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-08-11T15:42:22.444035Z

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.

source=pdf_text observed=2026-08-11T15:42:22.400994Z digest=sha256:357d57d8bd5fe9be897cbf4c1ee34cf139feb6ffd0946eb0ac9b3c2379bc8659

Observation 86d2865e-6e1e-40b3-ae79-098cb8d6d665 · inbound

FloodVision: Urban Flood Depth Estimation Using Foundation Vision-Language Models and Domain Knowledge Graph cites this paper.

FloodVision: Urban Flood Depth Estimation Using Foundation Vision-Language Models and Domain Knowledge Graph An Efficient Modern Baseline for FloodNet VQA

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-15T16:29:51.760963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:29:51.760963Z digest=sha256:2467aba1f833d530dbd0f0492b9a39415d84b8ce43610c40d720ec2bda60d451