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Paper Citation Record · LEDGER

Benchmarking Automatic Machine Learning Frameworks

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1808.06492.

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

pith.paper-citation-record.v1
1808.06492 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 2026-08-22T06:32:14.747728+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:42:58.854808Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T13:39:04.058480Z

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 fb2b6b78-7bc9-4e7e-b4af-788b46c8b35b · inbound

BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filtering cites this paper.

BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filtering Benchmarking Automatic Machine Learning Frameworks

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-08-06T13:39:04.269173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-06T13:39:00.427257Z digest=sha256:30d9be6ad465b5b04fba1edc8a431cb03c956476583b1e298b0dc74e18caef71

Observation a3c23d2b-5475-49a4-b26c-0fad48fd1fcd · inbound

AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data cites this paper.

AutoML-Med: A Framework for Automated Machine Learning in Medical Tabular Data Benchmarking Automatic Machine Learning Frameworks

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-15T17:42:58.854808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:42:58.854808Z digest=sha256:122cced0912ad1591fd450a501c34b037d20beb96f7ede79c18fdc7152d6318c