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Paper Citation Record · LEDGER

A Survey of Semantic Segmentation

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1602.06541.

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

pith.paper-citation-record.v1
1602.06541 v2

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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:33:21.515240Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T20:25:33.722299Z

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 bd828284-f833-4898-8ed1-c6bb86906513 · inbound

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation cites this paper.

A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation A Survey of Semantic Segmentation

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-08T19:33:21.515240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T19:33:21.515240Z digest=sha256:040f475a68f2e2865bd08361d1d721ead24501233e30e4e00ccd648ad3a8b3a4

Observation 94c23f3b-fc03-4b4d-aaa7-3abba4fc9f07 · inbound

From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images cites this paper.

From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images A Survey of Semantic Segmentation

Reference 9

Resolution
malformed identifier
local_arxiv, observed 2026-08-06T20:25:33.811259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-06T20:25:33.506338Z digest=sha256:c2fd71e6598af0ea6979f6a2b84ffb203cca83cfaed33c14b006f9a2bf936a34