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Paper Citation Record · LEDGER

Fast covariance-free spatiotemporal modeling via coarse-to-fine learning

As of 17 August 2026, this Paper Citation Record lists 2 of 2 outbound references and 0 inbound Pith citation observations for arXiv:2608.03449.

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

pith.paper-citation-record.v1
2608.03449 v1

Coverage vector

measured 2 of 2 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T19:02:36.706003Z

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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

2 of 2 outbound references displayed

  • verified exact1
  • verified fuzzy0
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9ff1e73f-a15f-4dfb-91a8-def7bcd12529 · outbound

This paper cites Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions.

Fast covariance-free spatiotemporal modeling via coarse-to-fine learning Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-05T19:02:36.702764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T19:02:36.702764Z digest=sha256:6793ae17125d3bdeaff95b5e8f02b28d2f86a938232e6623ea9c12763647ab3e

Observation 0076397b-8845-451e-a0a3-0d3f1494d40b · outbound

This paper cites Coarse-to-fine spatial GLMM for scalable prediction and multiscale analysis.

Fast covariance-free spatiotemporal modeling via coarse-to-fine learning Coarse-to-fine spatial GLMM for scalable prediction and multiscale analysis

Reference 15

Resolution
verified exact
local_arxiv, observed 2026-08-05T19:02:36.729296Z

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=2026-08-05T19:02:36.706003Z digest=sha256:659e1157700a4d66105553f115749ebbf90901c734632d5ee352dac000cd6016

Pith citing papers

No inbound Pith citation observations are available.