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Paper Citation Record · LEDGER

Interpretable Pre-Trained Transformers for Heart Time-Series Data

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.20775.

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

pith.paper-citation-record.v1
2407.20775 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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T16:38:50.662271Z

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

2
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 793ee55c-31f4-4fe7-9168-fff4a2f3ba10 · inbound

Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings cites this paper.

Pulse-PPG: An Open-Source Field-Trained PPG Foundation Model for Wearable Applications Across Lab and Field Settings Interpretable Pre-Trained Transformers for Heart Time-Series Data

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-09T16:38:50.662271Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T16:38:50.662271Z digest=sha256:e1a82f3832a7940bc68514878a7442c8124ff0b8d6019b672954138758307eab

Observation 30456234-0314-479e-aae2-012ecaa18537 · inbound

Modeling Day-Long ECG Signals to Predict Heart Failure Risk with Explainable AI cites this paper.

Modeling Day-Long ECG Signals to Predict Heart Failure Risk with Explainable AI Interpretable Pre-Trained Transformers for Heart Time-Series Data

Reference 40

Resolution
verified exact
local_arxiv, observed 2026-08-03T15:04:04.013381Z

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.

source=pdf_text observed=2026-08-03T15:01:43.970494Z digest=sha256:67aad9eb252e7f8a3edfb6f3fc41308bc870d4622deae2c8f656150782e1bb63