Pith. sign in

Paper Citation Record · LEDGER

A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.08808.

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

pith.paper-citation-record.v1
2403.08808 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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T14:33:45.737662Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T20:26:16.626275Z

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 03b58485-9317-46f1-bb54-bcfc44aa1a0b · inbound

Hardware-in-the-loop Simulation Testbed for Geomagnetic Navigation cites this paper.

Hardware-in-the-loop Simulation Testbed for Geomagnetic Navigation A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-11T14:33:45.737662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:33:45.737662Z digest=sha256:df88ac270669cd077fd4ad01299e429e379b116329640ec44918abcd1bd36a1e

Observation f8c08332-7cce-45b5-a3e4-d24e3f72f213 · inbound

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation cites this paper.

Exploring the Generalizability of Geomagnetic Navigation: A Deep Reinforcement Learning approach with Policy Distillation A Bionic Data-driven Approach for Long-distance Underwater Navigation with Anomaly Resistance

Reference 8

Resolution
verified exact
local_arxiv, observed 2026-08-08T20:26:16.631308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-08T20:26:15.479641Z digest=sha256:6098b33ae4605fcfc074e912cc463fd9691a965bdd13978bd333b75fd8026bb8