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Paper Citation Record · LEDGER

How to avoid machine learning pitfalls: a guide for academic researchers

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2108.02497.

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

pith.paper-citation-record.v1
2108.02497 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:03:44.082353Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T04:17:37.147374Z

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 c799a58f-57de-42e6-a190-97f981c357ab · inbound

Fairness in Federated Learning: Fairness for Whom? cites this paper.

Fairness in Federated Learning: Fairness for Whom? How to avoid machine learning pitfalls: a guide for academic researchers

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-07T13:45:18.043579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:45:18.043579Z digest=sha256:648b6728fa03cef2a41aee76908bb3563d2d69cbf32991d0ac6e5de48fb92add

Observation 5c26e7ba-e351-4aca-8757-e82c9f11d74e · inbound

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation cites this paper.

Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation How to avoid machine learning pitfalls: a guide for academic researchers

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-07T04:09:39.895768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:09:39.895768Z digest=sha256:6ad3a94d5141e6bd53e6ca30b026fdf6ea91252b3c00bdf7e2b8ecf5b31d3743

Observation 725b44ee-6488-4e9e-9da7-85238a7c80c9 · inbound

Importance of User Control in Data-Centric Steering for Healthcare Experts cites this paper.

Importance of User Control in Data-Centric Steering for Healthcare Experts How to avoid machine learning pitfalls: a guide for academic researchers

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-07T15:03:44.082353Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T15:03:44.082353Z digest=sha256:7ad9fb8aaebd2f0fadcc14d2d61d84fb7afe73941f61f291d8b3ff11dcf82d01

Observation 634547a5-5b2d-4b93-86a5-78b342671488 · inbound

Towards a more realistic evaluation of machine learning models for bearing fault diagnosis cites this paper.

Towards a more realistic evaluation of machine learning models for bearing fault diagnosis How to avoid machine learning pitfalls: a guide for academic researchers

Reference 10

Resolution
verified exact
arxiv_id, observed 2026-05-21T21:20:38.659222Z

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=2026-05-21T21:20:13.685836Z digest=sha256:f25a4d95777ad112ce728250934674ca55f9694a61800d26cc90b4f80a98d476

Observation e1bcde93-a360-4efc-9d80-20cbc3960b17 · inbound

A prior-free blind detection of information leakage from model predictions cites this paper.

A prior-free blind detection of information leakage from model predictions How to avoid machine learning pitfalls: a guide for academic researchers

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-07-03T04:17:37.148806Z

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=2026-06-27T14:03:47.667998Z digest=sha256:2ffcca178eb993ed4e9a331dbcc57e6cb235b2e54a90b54d8de96f51ddef2544