Paper Citation Record · LEDGER

Learning to generate physical ocean states: Towards hybrid climate modeling

As of 19 August 2026, this Paper Citation Record lists 8 of 8 outbound references and 0 inbound Pith citation observations for arXiv:2502.02499.

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

pith.paper-citation-record.v1
2502.02499 v1

Coverage vector

measured 8 of 8 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:00:41.658944Z

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

8 of 8 outbound references displayed

verified exact0
verified fuzzy1
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6f8cfc1b-ffb9-4b4f-8d56-645fa966a994 · outbound

This paper cites Glonet: Mercator's end-to-end neural forecasting system, 2024.

Learning to generate physical ocean states: Towards hybrid climate modeling Glonet: Mercator's end-to-end neural forecasting system, 2024

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-09T12:00:41.618834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5860354b-7dfa-405f-9e6d-fa0c75b56b19 · outbound

This paper cites Denoising diffusion probabilistic models.

Learning to generate physical ocean states: Towards hybrid climate modeling Denoising diffusion probabilistic models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-09T12:00:41.624697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbede3ec-33b4-42da-8433-f5fad15e62fc · outbound

This paper cites Brenner, and Stephan Hoyer.

Learning to generate physical ocean states: Towards hybrid climate modeling Brenner, and Stephan Hoyer

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T12:00:41.629889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15405a7d-f3a3-4004-97a6-3344850a7d0e · outbound

This paper cites From Zero to Turbulence: Generative Modeling for 3D Flow Simulation.

Learning to generate physical ocean states: Towards hybrid climate modeling From Zero to Turbulence: Generative Modeling for 3D Flow Simulation

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-09T12:00:41.635124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97927a51-de26-4d28-8b8b-78d63b26eea1 · outbound

This paper cites U-Net : Convolutional networks for biomedical image segmentation.

Learning to generate physical ocean states: Towards hybrid climate modeling U-Net : Convolutional networks for biomedical image segmentation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T12:00:41.867517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6ca73334-1755-42ab-b078-fde006a766f6 · outbound

This paper cites Diffusers: State-of-the-art diffusion models.

Learning to generate physical ocean states: Towards hybrid climate modeling Diffusers: State-of-the-art diffusion models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-09T12:00:41.646944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84275e6a-c2cf-4827-bab0-32c45f033172 · outbound

This paper cites XiHe: A Data-Driven Model for Global Ocean Eddy-Resolving Forecasting.

Learning to generate physical ocean states: Towards hybrid climate modeling XiHe: A Data-Driven Model for Global Ocean Eddy-Resolving Forecasting

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-09T12:00:41.653413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 532d56f3-ff55-4109-bbc4-6916a13290b1 · outbound

This paper cites ACE: A fast, skillful learned global atmospheric model for climate prediction.

Learning to generate physical ocean states: Towards hybrid climate modeling ACE: A fast, skillful learned global atmospheric model for climate prediction

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T12:00:41.658944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.