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Paper Citation Record · LEDGER

How to Train BERT with an Academic Budget

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2104.07705.

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

pith.paper-citation-record.v1
2104.07705 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 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:50:20.379928Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-16T09:20:20.269612Z

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 0f4d96f1-b061-4e1b-ad94-8e80205140a0 · inbound

Demystifying CLIP Data cites this paper.

Demystifying CLIP Data How to Train BERT with an Academic Budget

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-05-16T09:20:20.271451Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-16T09:20:20.143143Z digest=sha256:3dbcc2f0fad9b1385ea0acf9d11e633b926bb2db1deb4f72b4f7216804eb85cd

Observation 453a4e5f-1dc6-41ba-a42e-07c54a7a8860 · inbound

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation cites this paper.

Prediction-powered estimators for finite population statistics in highly imbalanced textual data: Public hate crime estimation How to Train BERT with an Academic Budget

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-16T00:50:20.379928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:50:20.379928Z digest=sha256:ba2f0733a474eff6cfbc08a69cd7d2962611d5463a865fb195bc9c9d0e6f16c9