{"as_of":"2026-08-19T15:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:95dcf9a2f70ff4b9cbda0bc08a5d6f0351133f8dd906ed7ad0bdb17d050447be","coverage":[{"denominator":8,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":8,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T12:00:41.658944Z","state":"measured"},{"denominator":8,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":8,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2502.02499/citation-record","integrity":"/paper/2502.02499/integrity","json":"/paper/2502.02499/citation-record.json","paper":"/paper/2502.02499"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:00:41.618834Z","title":"Glonet: Mercator's end-to-end neural forecasting system, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02499","last_updated":"2025-02-04T17:14:41Z","snapshot_observed_at":"2026-08-14T08:46:59.738628Z","submitted_at":"2025-02-04T17:14:41Z","title":"Learning to generate physical ocean states: Towards hybrid climate modeling","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-09T12:00:41.618834Z"},"links":{"citing_paper":"/paper/2502.02499"},"observation_digest":"sha256:3299187865a7b4b422c142b53000b9edda13828bc26dc6ae4a661c0806edcefe","observation_id":"6f8cfc1b-ffb9-4b4f-8d56-645fa966a994","resolution":{"observed_at":"2026-08-09T12:00:41.618834Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:00:41.624697Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.02499","last_updated":"2025-02-04T17:14:41Z","snapshot_observed_at":"2026-08-14T08:46:59.738628Z","submitted_at":"2025-02-04T17:14:41Z","title":"Learning to generate physical ocean states: Towards hybrid climate modeling","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-09T12:00:41.624697Z"},"links":{"citing_paper":"/paper/2502.02499"},"observation_digest":"sha256:515035eba17b6f5e100704ebf68acd9505dba4dd259fe77fbb439399bc5801e5","observation_id":"5860354b-7dfa-405f-9e6d-fa0c75b56b19","resolution":{"observed_at":"2026-08-09T12:00:41.624697Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:00:41.629889Z","title":"Brenner, and Stephan Hoyer","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02499","last_updated":"2025-02-04T17:14:41Z","snapshot_observed_at":"2026-08-14T08:46:59.738628Z","submitted_at":"2025-02-04T17:14:41Z","title":"Learning to generate physical ocean states: Towards hybrid climate modeling","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-09T12:00:41.629889Z"},"links":{"citing_paper":"/paper/2502.02499"},"observation_digest":"sha256:4b5c27c4bceee4a582cae12902bb9ec265fd63c1161ee612a17dbe5e4a7d4819","observation_id":"dbede3ec-33b4-42da-8433-f5fad15e62fc","resolution":{"observed_at":"2026-08-09T12:00:41.629889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.01776","last_updated":"2024-03-14T22:46:25Z","snapshot_observed_at":"2026-08-19T11:46:08.835800Z","submitted_at":"2023-05-29T18:20:28Z","title":"From Zero to Turbulence: Generative Modeling for 3D Flow Simulation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.01776","snapshot_observed_at":"2026-08-09T12:00:41.635124Z","title":"From zero to turbulence: Generative modeling for 3d flow simulation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02499","last_updated":"2025-02-04T17:14:41Z","snapshot_observed_at":"2026-08-14T08:46:59.738628Z","submitted_at":"2025-02-04T17:14:41Z","title":"Learning to generate physical ocean states: Towards hybrid climate modeling","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-09T12:00:41.635124Z"},"links":{"cited_paper":"/paper/2306.01776","citing_paper":"/paper/2502.02499"},"observation_digest":"sha256:e12aaf35dc9086d6ad89ca7d08cf1ad47b1129af3ccac29de41f4fa873e1a91b","observation_id":"15405a7d-f3a3-4004-97a6-3344850a7d0e","resolution":{"observed_at":"2026-08-09T12:00:41.635124Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:00:41.860300Z","title":"U-Net : Convolutional networks for biomedical image segmentation","venue":null,"work_id":"5a76313a-4121-4531-a5b2-246a3563844f","year":2015},"citing_paper":{"arxiv_id":"2502.02499","last_updated":"2025-02-04T17:14:41Z","snapshot_observed_at":"2026-08-14T08:46:59.738628Z","submitted_at":"2025-02-04T17:14:41Z","title":"Learning to generate physical ocean states: Towards hybrid climate modeling","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-09T12:00:41.641718Z"},"links":{"citing_paper":"/paper/2502.02499"},"observation_digest":"sha256:d537983fdbf1fefc29bb91069e70bb3a05f3604e8a4c2ce6c9d541b80ff4fc39","observation_id":"97927a51-de26-4d28-8b8b-78d63b26eea1","resolution":{"observed_at":"2026-08-09T12:00:41.867517Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:00:41.646944Z","title":"Diffusers: State-of-the-art diffusion models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.02499","last_updated":"2025-02-04T17:14:41Z","snapshot_observed_at":"2026-08-14T08:46:59.738628Z","submitted_at":"2025-02-04T17:14:41Z","title":"Learning to generate physical ocean states: Towards hybrid climate modeling","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-09T12:00:41.646944Z"},"links":{"citing_paper":"/paper/2502.02499"},"observation_digest":"sha256:5c521cf60e9dcf8df31d8e4090a5f9ac58cdddc1c2e7e17d97e38c78744e284b","observation_id":"6ca73334-1755-42ab-b078-fde006a766f6","resolution":{"observed_at":"2026-08-09T12:00:41.646944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.02995","last_updated":"2024-10-22T09:29:56Z","snapshot_observed_at":"2026-08-19T02:22:15.763610Z","submitted_at":"2024-02-05T13:34:19Z","title":"XiHe: A Data-Driven Model for Global Ocean Eddy-Resolving Forecasting","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.02995","snapshot_observed_at":"2026-08-09T12:00:41.653413Z","title":"Xihe: A data-driven model for global ocean eddy-resolving forecasting","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.02499","last_updated":"2025-02-04T17:14:41Z","snapshot_observed_at":"2026-08-14T08:46:59.738628Z","submitted_at":"2025-02-04T17:14:41Z","title":"Learning to generate physical ocean states: Towards hybrid climate modeling","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-09T12:00:41.653413Z"},"links":{"cited_paper":"/paper/2402.02995","citing_paper":"/paper/2502.02499"},"observation_digest":"sha256:e0320ada64bbbdf074e685f87ce97dfdf9203f96ff402c985ce3c3d592f07315","observation_id":"84275e6a-c2cf-4827-bab0-32c45f033172","resolution":{"observed_at":"2026-08-09T12:00:41.653413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.02074","last_updated":"2023-12-06T21:33:12Z","snapshot_observed_at":"2026-08-16T14:55:28.964416Z","submitted_at":"2023-10-03T14:15:06Z","title":"ACE: A fast, skillful learned global atmospheric model for climate prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.02074","snapshot_observed_at":"2026-08-09T12:00:41.658944Z","title":"Ace: A fast, skillful learned global atmospheric model for climate prediction","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.02499","last_updated":"2025-02-04T17:14:41Z","snapshot_observed_at":"2026-08-14T08:46:59.738628Z","submitted_at":"2025-02-04T17:14:41Z","title":"Learning to generate physical ocean states: Towards hybrid climate modeling","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-09T12:00:41.658944Z"},"links":{"cited_paper":"/paper/2310.02074","citing_paper":"/paper/2502.02499"},"observation_digest":"sha256:3fb3b30e43050a51781c5cbbe9c4a543e6589172ab68a895c25ca53d7183139b","observation_id":"532d56f3-ff55-4109-bbc4-6916a13290b1","resolution":{"observed_at":"2026-08-09T12:00:41.658944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.02499","last_updated":"2025-02-04T17:14:41Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T08:46:59.738628Z","submitted_at":"2025-02-04T17:14:41Z","title":"Learning to generate physical ocean states: Towards hybrid climate modeling"},"reference_resolution":{"displayed":8,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":7,"verified_exact":0,"verified_fuzzy":1},"total_outbound_references":8},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"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."}