Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
The autofeat Python Library for Automated Feature Engineering and Selection
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1901.07329.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T19:10:50.858995Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T14:52:45.771191Z
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 03bdf279-87a0-4566-a66b-0dae4fdd5453 · inbound
Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation cites this paper.
Towards Data-Centric AI: A Comprehensive Survey of Traditional, Reinforcement, and Generative Approaches for Tabular Data Transformation The autofeat Python Library for Automated Feature Engineering and Selection
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T19:10:50.858995Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8dd2525b-3fdf-4889-8c4f-d3b9450d2ded · inbound
GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement cites this paper.
GPT-FT: An Efficient Automated Feature Transformation Using GPT for Sequence Reconstruction and Performance Enhancement The autofeat Python Library for Automated Feature Engineering and Selection
Reference 11
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-05T14:52:45.775569Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.