Paper Citation Record · LEDGER

Deep Multi-fidelity Gaussian Processes

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1604.07484.

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

pith.paper-citation-record.v1
1604.07484 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 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 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:19:08.273621Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T16:41:06.435155Z

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 5d5eafe9-4b42-4dcf-9afa-5bd7e255a38c · inbound

A Graph Neural Network Surrogate Model for Multi-Objective Fluid-Acoustic Shape Optimization cites this paper.

A Graph Neural Network Surrogate Model for Multi-Objective Fluid-Acoustic Shape Optimization Deep Multi-fidelity Gaussian Processes

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-11T10:21:24.538633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 672622bf-7613-4d68-9185-3b561e6a48d9 · inbound

Data-Driven Structural State Estimation via Multi-Fidelity Gaussian Process Models cites this paper.

Data-Driven Structural State Estimation via Multi-Fidelity Gaussian Process Models Deep Multi-fidelity Gaussian Processes

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-16T04:19:08.273621Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 707a3ccc-7ab9-4c19-911d-4517a44c104f · inbound

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data cites this paper.

Multifidelity-Augmented Gaussian Process Inputs for Surrogate Modeling from Scarce Data Deep Multi-fidelity Gaussian Processes

Reference 75

Resolution: unresolved; no resolver link, observed 2026-07-13T20:27:45.196743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08c2d45c-9f4f-4df7-9340-15ae335c4984 · inbound

A new framework for non-stationary spatio-temporal data fusion of multi-fidelity models cites this paper.

A new framework for non-stationary spatio-temporal data fusion of multi-fidelity models Deep Multi-fidelity Gaussian Processes

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:59:20.549160Z

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.