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Paper Citation Record · LEDGER

Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1707.02968.

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

pith.paper-citation-record.v1
1707.02968 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:32:02.075560Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

304
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c1068930-e497-416e-9b18-6666852262f3 · inbound

The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics? cites this paper.

The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics? Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T16:32:02.075560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:32:02.075560Z digest=sha256:f18daf8c8d04f9cba59561aaef8840a635171009cdad54a976f33f5a284b6300

Observation 180a3b86-3611-4b6e-9c3a-3363bec69801 · inbound

Toward Calibrated, Fair, and accurate Deepfake Detection cites this paper.

Toward Calibrated, Fair, and accurate Deepfake Detection Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

Reference 264

Resolution
verified exact
local_arxiv, observed 2026-06-28T07:11:45.285236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-06-28T07:05:18.026601Z digest=sha256:be35c49c5935e15cd2fab5d2e6bbe04c8bf8fd108628e01d661a962e4bd0e022