Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2203.06889.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T12:20:25.823858Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T14:27:19.421202Z
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 153eafef-f50a-487b-a32f-acf4b897ccc8 · inbound
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling cites this paper.
ECG-Byte: A Tokenizer for End-to-End Generative Electrocardiogram Language Modeling Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T12:20:25.823858Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1f7a61b0-b70e-48c3-bd32-87b67ae730ee · inbound
Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Framework cites this paper.
Signal, Image, or Symbolic: Exploring the Best Input Representation for Electrocardiogram-Language Models Through a Unified Framework Lead-agnostic Self-supervised Learning for Local and Global Representations of Electrocardiogram
Reference 34
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T14:27:19.532695Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.