Paper Citation Record · LEDGER

W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2304.08754.

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

pith.paper-citation-record.v1
2304.08754 v2

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 2026-08-14T06:32:32.682623+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:24:59.689917Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T13:35:46.110066Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 06226ae7-4ed1-47f3-88d3-981588f83a92 · inbound

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation cites this paper.

Leadsee-Precip: A Deep Learning Diagnostic Model for Precipitation W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T17:24:59.689917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb8f9f80-2910-4810-a6cd-9326aeef7b9a · inbound

Self-Supervised Learning with Probabilistic Density Labeling for Rainfall Probability Estimation cites this paper.

Self-Supervised Learning with Probabilistic Density Labeling for Rainfall Probability Estimation W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T20:25:13.598168Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9925b2b1-df30-4a68-9223-97ca8dc0fea9 · inbound

Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting cites this paper.

Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-11T11:00:56.604791Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e04091b-1ba7-4c73-8961-52e699d8e018 · inbound

Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities cites this paper.

Spatio-Temporal Foundation Models: Vision, Challenges, and Opportunities W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T20:23:00.145917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48ce62a4-f0d3-4569-afe2-123d67fe5431 · inbound

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

Earth Science Foundation Models: From Perception to Reasoning and Discovery W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-14T22:08:03.547529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7dedceb2-7c0f-4e4d-8803-18a53b51b780 · inbound

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

Earth Science Foundation Models: From Perception to Reasoning and Discovery W-MAE: Pre-trained weather model with masked autoencoder for multi-variable weather forecasting

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-07-01T13:35:46.111826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.