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Paper Citation Record · LEDGER

Diagnosing Model Performance Under Distribution Shift

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2303.02011.

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

pith.paper-citation-record.v1
2303.02011 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T00:51:05.033656Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T09:10:59.346861Z

Reference resolution

0 of 0 outbound references displayed

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  • verified fuzzy0
  • unresolved0
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  • 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 7a03e858-6014-4227-ab8e-82e65b51c2b1 · inbound

U-aggregation: Unsupervised Aggregation of Multiple Learning Algorithms cites this paper.

U-aggregation: Unsupervised Aggregation of Multiple Learning Algorithms Diagnosing Model Performance Under Distribution Shift

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-10T00:51:05.033656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-10T00:51:05.033656Z digest=sha256:1283a63e0dc6f62cbff4cb58211f645ab16d45d5159e7b3054e0eabee316072c

Observation 57071a91-97b6-47d2-b2eb-68a92ec7c0af · inbound

Realistic Evaluation of TabPFN v2 in Open Environments cites this paper.

Realistic Evaluation of TabPFN v2 in Open Environments Diagnosing Model Performance Under Distribution Shift

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T15:08:13.082707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:08:13.082707Z digest=sha256:f04eea5d6bccf0b495f3eaea27c245cf6efdfbacf501ba549a3780ec9fc9caf8

Observation 07b64a80-f40e-43f7-a471-c11944316d2a · inbound

Explaining Concept Shift with Interpretable Feature Attribution cites this paper.

Explaining Concept Shift with Interpretable Feature Attribution Diagnosing Model Performance Under Distribution Shift

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-07T13:56:35.235536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:56:35.235536Z digest=sha256:6c0d2bef42ce9364cbcc0f75373a73ef0d2457aed8ddfd923240fd50dcdf1cd3

Observation b029dc02-db58-4033-b194-8af9a0245f62 · inbound

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift cites this paper.

"Who experiences large model decay and why?" A Hierarchical Framework for Diagnosing Heterogeneous Performance Drift Diagnosing Model Performance Under Distribution Shift

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T12:04:30.799496Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:04:30.799496Z digest=sha256:cfbf583c0f2ea22c31f30e0885a4fd552ed5e532bad3abe9402f588e1d37a65a

Observation 2f115867-2166-40e9-a734-5b4d4805b3e6 · inbound

Data Heterogeneity Modeling for Trustworthy Machine Learning cites this paper.

Data Heterogeneity Modeling for Trustworthy Machine Learning Diagnosing Model Performance Under Distribution Shift

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T11:58:33.223518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:58:33.223518Z digest=sha256:ef2461b259968f55ae93d25b7394a6391b8e659018476243c29b86ab7a2543d0

Observation 383c99aa-ce44-430d-9452-ff8568a112b1 · inbound

Uncovering Bias Mechanisms in Observational Studies cites this paper.

Uncovering Bias Mechanisms in Observational Studies Diagnosing Model Performance Under Distribution Shift

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T11:56:22.656281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:56:22.656281Z digest=sha256:e2bdcd50eee0d8310759fb7154faf24241a851f054097c1f543a510096f7d188

Observation 0cb00691-98cb-4c59-822d-ffe8536943a9 · inbound

General and Estimable Learning Bound Unifying Covariate and Concept Shifts cites this paper.

General and Estimable Learning Bound Unifying Covariate and Concept Shifts Diagnosing Model Performance Under Distribution Shift

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T00:47:16.777113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T00:47:16.777113Z digest=sha256:7614a8f40abddb53e6fa7f3ad109db227aba8a81999e7970c4e6bda48c05c113

Observation 3bc0ac7b-7f47-4472-9094-4e0ec7625b97 · inbound

Data Curation Matters: Model Collapse and Spurious Shift Performance Prediction from Training on Uncurated Text Embeddings cites this paper.

Data Curation Matters: Model Collapse and Spurious Shift Performance Prediction from Training on Uncurated Text Embeddings Diagnosing Model Performance Under Distribution Shift

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-06T23:28:49.824756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:28:49.824756Z digest=sha256:70cbf41e5cc44784bff0ca345360ca899a09e25b441b5c3f35f28a4279335b3a

Observation c58ab6a3-cbfb-4ff2-a9ed-f464cf7b20a0 · inbound

When the Past Misleads: Rethinking Training Data Expansion Under Temporal Distribution Shifts cites this paper.

When the Past Misleads: Rethinking Training Data Expansion Under Temporal Distribution Shifts Diagnosing Model Performance Under Distribution Shift

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-05T13:00:04.021229Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T13:00:04.021229Z digest=sha256:8f18a9cc650de75b2e2475adfdbddd7385df45884962669b1031feed1efad7f9

Observation cdc66e20-7b2c-4155-9806-339f41c897bf · inbound

ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values cites this paper.

ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values Diagnosing Model Performance Under Distribution Shift

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-11T09:10:59.350770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-10T16:13:22.301367Z digest=sha256:7fcee917483a0896d0b76721dca761e1b9304e41f0d2f99bd14451c63b7a75ee