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Paper Citation Record · LEDGER

Applications of Deep Learning to physics workflows

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.08106.

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

pith.paper-citation-record.v1
2306.08106 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 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:45:53.059987Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

1
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cd3618d7-3bf3-4b41-b85e-a16027e879f0 · inbound

Can Transformers help us perform parameter estimation of overlapping signals in gravitational wave detectors? cites this paper.

Can Transformers help us perform parameter estimation of overlapping signals in gravitational wave detectors? Applications of Deep Learning to physics workflows

Reference 18

Resolution
unresolved
no resolver link, observed 2026-08-16T00:45:53.059987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:45:53.059987Z digest=sha256:1c6f68a9e6b74cafb1b288883420117e4559bb943f00e0aad1fcb85812444366

Observation cfdb863a-78dc-4854-8f39-130e1e7197c9 · inbound

From stellar light to astrophysical insight: automating variable star research with machine learning cites this paper.

From stellar light to astrophysical insight: automating variable star research with machine learning Applications of Deep Learning to physics workflows

Reference 8

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T20:22:42.955918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-08-06T20:22:25.560085Z digest=sha256:8a344f5391d80f02d7f2e286b4ce13e9d953142f355581d3490648736e032006