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Paper Citation Record · LEDGER

DGP-LVM: Derivative Gaussian process latent variable models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.04074.

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

pith.paper-citation-record.v1
2404.04074 v3

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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:59:51.684623Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:59:51.846870Z

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 722a7c65-e282-470e-89a0-1145fd270074 · inbound

Multi-Output Gaussian Processes for Graph-Structured Data cites this paper.

Multi-Output Gaussian Processes for Graph-Structured Data DGP-LVM: Derivative Gaussian process latent variable models

Reference 35

Resolution
verified exact
local_arxiv, observed 2026-08-07T14:59:51.889658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T14:59:51.684623Z digest=sha256:39e9b1c076a9adf1d6531180f20562b55bf4afc7fc18cb6215d79b99cb03206c

Observation 11eb4e8f-0653-47f8-825f-1c7d5fb4d18a · inbound

A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications cites this paper.

A Streaming Sparse Cholesky Method for Derivative-Informed Gaussian Process Surrogates Within Digital Twin Applications DGP-LVM: Derivative Gaussian process latent variable models

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-04T00:38:16.917196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T00:38:16.917196Z digest=sha256:08917473dfab432088df04d68392c26f2f1e350f9cbd37c69f04199a11a7de6d