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Paper Citation Record · LEDGER

PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1703.00512.

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

pith.paper-citation-record.v1
1703.00512 v1

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 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:58:57.897629Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T15:19:42.367200Z

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 ad30daf6-7902-46f9-836a-680ec8e29cef · inbound

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback cites this paper.

Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

Reference 39

Resolution
metadata mismatch
local_arxiv, observed 2026-08-07T15:19:42.415935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=pdf_text observed=2026-08-07T15:19:40.907317Z digest=sha256:0fecebe53cd2aee2f4ea0a6b04ecf27ca64050d71563ed1e363d1ec71df7e0ed

Observation 833c2d83-01c8-4358-9123-f9dff3665961 · inbound

Data-Efficient Symbolic Regression via Foundation Model Distillation cites this paper.

Data-Efficient Symbolic Regression via Foundation Model Distillation PMLB: A Large Benchmark Suite for Machine Learning Evaluation and Comparison

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-15T16:58:57.897629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T16:58:57.897629Z digest=sha256:b7de4a0b5df02039729c141650a73fb16ebe9ad4498dcca352fdea56b48fed56