Paper Citation Record · LEDGER

Foundation Models for Geophysics: Review and Perspective

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.03163.

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

pith.paper-citation-record.v1
2406.03163 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:45:23.655479Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T13:35:45.961069Z

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 ad953b2d-1be1-4e0c-8b21-d35ca7ce7a1a · inbound

PEAR: Equal Area Weather Forecasting on the Sphere cites this paper.

PEAR: Equal Area Weather Forecasting on the Sphere Foundation Models for Geophysics: Review and Perspective

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:23.655479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 250d3777-7281-4779-910f-9d970e281322 · inbound

Ocean-E2E: Hybrid Physics-Based and Data-Driven Global Forecasting of Extreme Marine Heatwaves with End-to-End Neural Assimilation cites this paper.

Ocean-E2E: Hybrid Physics-Based and Data-Driven Global Forecasting of Extreme Marine Heatwaves with End-to-End Neural Assimilation Foundation Models for Geophysics: Review and Perspective

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:20.639551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6b23698-4f62-4f7c-adb0-0920ea8027d4 · inbound

Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding cites this paper.

Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding Foundation Models for Geophysics: Review and Perspective

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T21:24:37.176666Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7c829956-55a2-4747-a357-893d98ce1bec · inbound

Earth Science Foundation Models: From Perception to Reasoning and Discovery cites this paper.

Earth Science Foundation Models: From Perception to Reasoning and Discovery Foundation Models for Geophysics: Review and Perspective

Reference 297

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T22:08:03.453151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation eefcf4e7-2a75-48fb-8b25-ffebb969bc74 · inbound

Earth Science Foundation Models: From Perception to Reasoning and Discovery cites this paper.

Earth Science Foundation Models: From Perception to Reasoning and Discovery Foundation Models for Geophysics: Review and Perspective

Reference 297

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T13:35:45.962655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fcd34014-b3c3-42c3-9033-be1e1820d6cd · inbound

Pretrain-to-alignment learning paradigm to improve geophysical AI applicability under scarce field labels and synthetic-to-field gaps: A case study of relative geologic time estimation in global shelf-edge clinothems cites this paper.

Pretrain-to-alignment learning paradigm to improve geophysical AI applicability under scarce field labels and synthetic-to-field gaps: A case study of relative geologic time estimation in global shelf-edge clinothems Foundation Models for Geophysics: Review and Perspective

Reference 137

Resolution: verified exact; arxiv_id, observed 2026-05-19T19:37:43.794243Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.