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Paper Citation Record · LEDGER

Flood Prediction Using Classical and Quantum Machine Learning Models

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.01001.

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

pith.paper-citation-record.v1
2407.01001 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 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:30:37.867747Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T07:06:16.457014Z

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 0fd369a0-932d-454f-8b3e-4bfdbda60513 · inbound

Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region cites this paper.

Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region Flood Prediction Using Classical and Quantum Machine Learning Models

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-12T11:30:37.867747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T11:30:37.867747Z digest=sha256:7be73074074ee6aa284b7544ec9a85aa2db13da962ff1657bc36e5f1ba8a4cbc

Observation a1365fe3-90ce-4084-95e6-bf02278d0726 · inbound

Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification cites this paper.

Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification Flood Prediction Using Classical and Quantum Machine Learning Models

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-11T07:06:16.464938Z

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.

source=pdf_text observed=2026-05-10T17:17:15.552151Z digest=sha256:6a4ce7cd86fd77b6acaf57c9177c869f52680a53497a0b44a0bae2960bb88832