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Paper Citation Record · LEDGER

tf.data: A Machine Learning Data Processing Framework

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2101.12127.

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

pith.paper-citation-record.v1
2101.12127 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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:14:35.334066Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-22T21:15:09.603008Z

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 0ecf53de-5d5d-4554-aa1e-9b436101f1bf · inbound

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput cites this paper.

Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput tf.data: A Machine Learning Data Processing Framework

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-08T14:14:35.334066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:14:35.334066Z digest=sha256:40b9df9c8838ebc565bb545fe4a615a1429b0176f6fd00343f3cb40dc3f3541e

Observation c5c4b6bd-954d-4c59-9536-79047b68b25a · inbound

MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training cites this paper.

MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training tf.data: A Machine Learning Data Processing Framework

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-22T21:15:09.605303Z

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=2026-05-22T21:12:22.201810Z digest=sha256:6a9cd9b475b333aed3902ac54283bae051332a0457bb25bc98c750be38c3adf7