Pith. sign in

Paper Citation Record · LEDGER

The VIA Annotation Software for Images, Audio and Video

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1904.10699.

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

pith.paper-citation-record.v1
1904.10699 v3

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 2026-08-15T14:53:07.885243Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T15:46:42.976374Z

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 e40d4460-e6d2-4a9d-a7bc-f54cc3507226 · inbound

Toward quantitative fractography using convolutional neural networks cites this paper.

Toward quantitative fractography using convolutional neural networks The VIA Annotation Software for Images, Audio and Video

Reference 43

Resolution
verified exact
local_arxiv, observed 2026-08-14T15:46:42.981500Z

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=2026-08-14T15:46:42.895329Z digest=sha256:4dd0310dd0213909c7aef92e015e765528359c4f346106db329dc473a9b72f60

Observation b6411a83-b792-48b8-9f27-5f48668c3923 · inbound

Advancing Utility Pole and Sign Detection Through Deep Learning cites this paper.

Advancing Utility Pole and Sign Detection Through Deep Learning The VIA Annotation Software for Images, Audio and Video

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-15T14:53:07.885243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:53:07.885243Z digest=sha256:748f56e1d48c4df19f816b920438a6d00b91410fdb37d23ee0a4b6f836f2d1d7