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Paper Citation Record · LEDGER

Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2106.00417.

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

pith.paper-citation-record.v1
2106.00417 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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:47:37.037696Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T14:22:24.674257Z

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 e16d660b-6e64-4ff3-86b5-e2ca26e8fac0 · inbound

BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition cites this paper.

BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-11T23:47:37.037696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T23:47:37.037696Z digest=sha256:28ee9a615d4bdc9fb263fdd28a58614b78972020ee951355ad463ee9529d3a60

Observation e9afa9a9-3160-4da2-9032-80561a8f2993 · inbound

Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? cites this paper.

Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements Translate to Improvements on Similar Datasets? Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners

Reference 51

Resolution
verified exact
local_arxiv, observed 2026-08-10T14:22:24.679843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-10T14:22:24.552938Z digest=sha256:5af9a6c72bec2df7f7b22879551362c76c6d107371d81335e899708c86bb2260