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Paper Citation Record · LEDGER

Deconfusing intensity maps with neural networks

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1905.10376.

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

pith.paper-citation-record.v1
1905.10376 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 2026-08-14T10:47:27.871193Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-12T18:48:42.800312Z

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 a0dea8e3-f0a5-4bd0-90c3-9b093cb76021 · inbound

Cosmological parameter estimation from large-scale structure deep learning cites this paper.

Cosmological parameter estimation from large-scale structure deep learning Deconfusing intensity maps with neural networks

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-14T10:47:27.871193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T10:47:27.871193Z digest=sha256:a95fc703269ef6286517f0508fa0e685949315613590e889aac5115bd3d12526

Observation 2288ef94-2843-4b60-bc6f-344ebdd47956 · inbound

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution cites this paper.

AI-Powered Reconstruction of Dark Matter Velocity Fields from Redshift-Space Halo Distribution Deconfusing intensity maps with neural networks

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-08-12T18:48:42.884657Z

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=2026-08-12T18:48:42.420448Z digest=sha256:80d51b8e86aa1d3c79408653af41d593c0b6dbb43d7f560d9222a8ab9fb218a6