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Paper Citation Record · LEDGER

Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.14775.

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

pith.paper-citation-record.v1
2406.14775 v2

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 2026-08-20T06:33:59.587034+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:11:38.005946Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

0
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 74262289-4a80-4610-8a28-4d58a1581ad5 · inbound

WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks cites this paper.

WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation

Reference 81

Resolution
unresolved
no resolver link, observed 2026-08-11T23:11:38.005946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:11:38.005946Z digest=sha256:0c3a02a331c03874b873c443ee31a366af6b42332dde1b424aacc770e3318c5a

Observation be8734fc-eb31-4ed5-ace2-c75513f4f609 · inbound

Finetuning AI Foundation Models to Develop Subgrid-Scale Parameterizations: A Case Study on Atmospheric Gravity Waves cites this paper.

Finetuning AI Foundation Models to Develop Subgrid-Scale Parameterizations: A Case Study on Atmospheric Gravity Waves Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation

Reference 26

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:43:16.463096Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-08-05T10:43:15.774558Z digest=sha256:0ceb1ad11cbab818652f614a6db3fa0d5ac4cc5c26de2bfb79bbd064760b0e62