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Paper Citation Record · LEDGER

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

As of 15 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 2026-08-15T06:32:42.880941+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=sha256:7dfc4e2fb9afaf070484e7c86842a78ea64318b6f9133d963652ba41a1d1702a

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 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-05T19:02:36.706003Z digest=sha256:463bfce34ae746a023cca03333b708fb895c6c030fe2b48c8d7b95de64771da8

Pith citing papers

No inbound Pith citation observations are available.