Pith. sign in

Paper Citation Record · LEDGER

Structured Consistency Loss for semi-supervised 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:2001.04647.

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

pith.paper-citation-record.v1
2001.04647 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 2026-08-07T15:28:46.467744Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:09:19.726073Z

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 697ea9bb-1f00-4592-ad07-f67c8d872d8e · inbound

P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation cites this paper.

P3Net: Progressive and Periodic Perturbation for Semi-Supervised Medical Image Segmentation Structured Consistency Loss for semi-supervised semantic segmentation

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-07T15:28:46.467744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:28:46.467744Z digest=sha256:c3bf8c3350a9a17fef5b615b41b1f991df22763486871eee7c5b1cd76573b98e

Observation bce203aa-ed4c-45f7-8081-c6ef84b86c7c · inbound

Efficient Prototype Consistency Learning in Medical Image Segmentation via Joint Uncertainty and Data Augmentation cites this paper.

Efficient Prototype Consistency Learning in Medical Image Segmentation via Joint Uncertainty and Data Augmentation Structured Consistency Loss for semi-supervised semantic segmentation

Reference 21

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T15:09:19.827873Z

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=2026-08-07T15:09:13.870736Z digest=sha256:3cfe3976838c5d1f9a87f64843c8c8bb76de26eeeedef3bdb30ac42b7a2cf21b