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Paper Citation Record · LEDGER

Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2201.04234.

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

pith.paper-citation-record.v1
2201.04234 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T22:31:18.363313Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T01:37:22.648629Z

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 bc9bbb23-a63b-4404-bfcd-b02b796e08f9 · inbound

Linear Correlation in LM's Compositional Generalization and Hallucination cites this paper.

Linear Correlation in LM's Compositional Generalization and Hallucination Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-08T22:31:18.363313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T22:31:18.363313Z digest=sha256:6b10d9713f48faf6cc2f71289eaa2470c5660e5b88e7d469d3eaecec0b2bde66

Observation b95d07ad-659b-4d83-bf23-39a6ad480540 · inbound

Unsupervised Source-Free Ranking of Biomedical Segmentation Models Under Distribution Shift cites this paper.

Unsupervised Source-Free Ranking of Biomedical Segmentation Models Under Distribution Shift Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-05-23T01:37:22.652834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-23T01:36:12.509353Z digest=sha256:a03d8f163bf71a10b69fb36ba2eb6be6459cce84474234c7c7b06587ee200bdc

Observation 8505c97c-1384-4598-9fc2-ad0ed630f3f5 · inbound

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings cites this paper.

Suitability Filter: A Statistical Framework for Classifier Evaluation in Real-World Deployment Settings Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-07T13:20:19.789920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:20:19.789920Z digest=sha256:7443c3c185d68b44f4712a1a81e131c094d0486428d4eaed475a12ba9d9d9554

Observation fe82142f-b97d-425e-9710-f0eb2b7673b2 · inbound

From Tea Leaves to System Maps: A Survey and Framework on Context-aware Machine Learning Monitoring cites this paper.

From Tea Leaves to System Maps: A Survey and Framework on Context-aware Machine Learning Monitoring Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T04:21:55.877030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:21:55.877030Z digest=sha256:11f875344cd8dd0b5e9db3599148bb09fdd8a487d57b97fe2ee25d66cd2068f1

Observation 19d35ce4-2da1-4e5c-b3ee-4e7f8b99cfdd · inbound

One task to rule them all: A closer look at traffic classification generalizability cites this paper.

One task to rule them all: A closer look at traffic classification generalizability Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-06T19:08:49.648306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:08:49.648306Z digest=sha256:6aeba836e75f2c0390d91d96c6e60a5e024f27b2b34ea04cc3e92dccc266e95e

Observation e580284f-52b6-48d4-bd89-baae983cb2cd · inbound

LanePerf: a Performance Estimation Framework for Lane Detection cites this paper.

LanePerf: a Performance Estimation Framework for Lane Detection Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-06T16:40:09.520773Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:40:09.520773Z digest=sha256:e53eac146e51054fe67c14b68b5753a92ef21a4580b852f8fd33938c359b2260

Observation d1c03d5f-31ad-4486-8ceb-c1c3807cf58a · inbound

Transductive Model Selection under Prior Probability Shift cites this paper.

Transductive Model Selection under Prior Probability Shift Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T11:31:14.578045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:31:14.578045Z digest=sha256:10e619f6f1bd1e47f2906cd5c825066bde10576ca75efacc6921d0284bfeaa7d

Observation e125aa13-473d-46cf-b38a-2799056d6207 · inbound

Inside-Out: Measuring Generalization in Vision Transformers Through Inner Workings cites this paper.

Inside-Out: Measuring Generalization in Vision Transformers Through Inner Workings Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T00:30:54.120203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T18:28:49.855280Z digest=sha256:a522e4ceaba47a3b19c61974ac3a287649e19f63433bf47bc7b2d718e627be99