{"as_of":"2026-08-14T11:55:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8e4384f04d7405e5114a775b2de0dd3036e89535ca9a7964776b59728a0e0cc6","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":10,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":10,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":10,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":10,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T22:12:31.880870Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":20,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2308.13280","last_updated":"2023-09-07T11:46:17Z","snapshot_observed_at":"2026-08-13T10:27:48.088578Z","submitted_at":"2023-08-25T10:02:26Z","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13280","snapshot_observed_at":"2026-08-11T22:12:31.880870Z","title":"Lessig, I","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.03743","last_updated":"2025-04-27T12:58:08Z","snapshot_observed_at":"2026-08-11T22:05:51.380380Z","submitted_at":"2024-12-04T22:23:17Z","title":"A Hybrid Deep-Learning Model for El Ni\\~no Southern Oscillation in the Low-Data Regime","version":2},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T22:12:31.880870Z"},"links":{"cited_paper":"/paper/2308.13280","citing_paper":"/paper/2412.03743"},"observation_digest":"sha256:57aea30bb23fc097d29ae196b29213ab898839a8c42f1ae22b7a530bb325cd91","observation_id":"827742c6-9224-43a8-86ac-64bd8d9e5279","resolution":{"observed_at":"2026-08-11T22:12:31.880870Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13280","last_updated":"2023-09-07T11:46:17Z","snapshot_observed_at":"2026-08-13T10:27:48.088578Z","submitted_at":"2023-08-25T10:02:26Z","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13280","snapshot_observed_at":"2026-08-11T13:36:04.858541Z","title":"Atmorep: A stochastic model of atmosphere dynamics using large scale representation learning","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.12971","last_updated":"2024-12-17T14:54:30Z","snapshot_observed_at":"2026-08-14T03:33:36.546544Z","submitted_at":"2024-12-17T14:54:30Z","title":"ArchesWeather & ArchesWeatherGen: a deterministic and generative model for efficient ML weather forecasting","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-11T13:36:04.858541Z"},"links":{"cited_paper":"/paper/2308.13280","citing_paper":"/paper/2412.12971"},"observation_digest":"sha256:7d33f787bbc5f502b65a6b57474763590e7119d65ed7359bd068371d417812cd","observation_id":"3e93d07c-dc76-48bc-bb84-0cd60f8b7409","resolution":{"observed_at":"2026-08-11T13:36:04.858541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13280","last_updated":"2023-09-07T11:46:17Z","snapshot_observed_at":"2026-08-13T10:27:48.088578Z","submitted_at":"2023-08-25T10:02:26Z","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13280","snapshot_observed_at":"2026-08-06T19:26:47.919155Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.05753","last_updated":"2025-07-08T07:57:08Z","snapshot_observed_at":"2026-08-07T05:27:15.719764Z","submitted_at":"2025-07-08T07:57:08Z","title":"Jigsaw: Training Multi-Billion-Parameter AI Weather Models with Optimized Model Parallelism","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T19:26:47.919155Z"},"links":{"cited_paper":"/paper/2308.13280","citing_paper":"/paper/2507.05753"},"observation_digest":"sha256:e9b62bb55d396063113eca19532f9bc321a697c511c2daa24d0824c28765ad2f","observation_id":"7feee98e-44d1-4198-8e25-e85e76f5e312","resolution":{"observed_at":"2026-08-06T19:26:47.919155Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13280","last_updated":"2023-09-07T11:46:17Z","snapshot_observed_at":"2026-08-13T10:27:48.088578Z","submitted_at":"2023-08-25T10:02:26Z","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13280","snapshot_observed_at":"2026-08-06T16:59:53.720865Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2507.12144","last_updated":"2025-07-18T08:05:51Z","snapshot_observed_at":"2026-08-09T02:46:35.827559Z","submitted_at":"2025-07-16T11:22:18Z","title":"FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T16:59:53.720865Z"},"links":{"cited_paper":"/paper/2308.13280","citing_paper":"/paper/2507.12144"},"observation_digest":"sha256:af14e4bd7410cb11df97857b7d9cb85ff476f5b7a478fa91a503b76ccdd1662b","observation_id":"a39f612e-b1da-4744-8f90-ac8cc6a99f0c","resolution":{"observed_at":"2026-08-06T16:59:53.720865Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13280","last_updated":"2023-09-07T11:46:17Z","snapshot_observed_at":"2026-08-13T10:27:48.088578Z","submitted_at":"2023-08-25T10:02:26Z","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13280","snapshot_observed_at":"2026-08-05T10:43:15.846420Z","title":", Luise, I","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.03816","last_updated":"2025-09-04T02:05:54Z","snapshot_observed_at":"2026-08-13T21:49:55.702816Z","submitted_at":"2025-09-04T02:05:54Z","title":"Finetuning AI Foundation Models to Develop Subgrid-Scale Parameterizations: A Case Study on Atmospheric Gravity Waves","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-05T10:43:15.846420Z"},"links":{"cited_paper":"/paper/2308.13280","citing_paper":"/paper/2509.03816"},"observation_digest":"sha256:ed329ae1e8ced32f661e7e578f3ff21058c4beb715509e4236e84419903f2966","observation_id":"1c0e825a-a6a1-4b2b-8ce7-31df5ba1924a","resolution":{"observed_at":"2026-08-05T10:43:15.846420Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13280","last_updated":"2023-09-07T11:46:17Z","snapshot_observed_at":"2026-08-13T10:27:48.088578Z","submitted_at":"2023-08-25T10:02:26Z","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.13280","snapshot_observed_at":"2026-08-03T08:33:30.406870Z","title":"Lessig, I","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.16598","last_updated":"2026-07-29T07:57:40Z","snapshot_observed_at":"2026-08-09T12:00:25.830309Z","submitted_at":"2026-01-23T10:00:49Z","title":"A robust and stable hybrid neural network/finite element method for 2D flows that generalizes to different geometries","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-03T08:33:30.406870Z"},"links":{"cited_paper":"/paper/2308.13280","citing_paper":"/paper/2601.16598"},"observation_digest":"sha256:484826e414d2af8df5d1ed7d43dcf0424b88e41887035e69552d1843386cd526","observation_id":"0771bb12-76a3-4c65-af54-0fb7dbd7a317","resolution":{"observed_at":"2026-08-03T08:33:30.406870Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13280","last_updated":"2023-09-07T11:46:17Z","snapshot_observed_at":"2026-08-13T10:27:48.088578Z","submitted_at":"2023-08-25T10:02:26Z","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning","version":2},"cited_work":{"arxiv_id":"2308.13280","doi":"10.48550/arxiv.2308.13280","metadata_source":"arxiv_reference","pith_arxiv_id":"2308.13280","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mathilde Leuridan, James Hawkes, Simon Smart, Emanuele Danovaro, Martin Schultz, and Tiago Quintino","venue":"arXiv (Cornell University)","work_id":"136c6f62-53ee-4363-901e-120664e00f2a","year":2023},"citing_paper":{"arxiv_id":"2604.04736","last_updated":"2026-04-06T15:03:35Z","snapshot_observed_at":"2026-08-11T16:43:14.763670Z","submitted_at":"2026-04-06T15:03:35Z","title":"Sampling Parallelism for Fast and Efficient Bayesian Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-10T18:48:24.778806Z"},"links":{"cited_paper":"/paper/2308.13280","citing_paper":"/paper/2604.04736"},"observation_digest":"sha256:006ceb5c6c00e1efbcf7a27899b5d1ba952e85b1858919ddc941c614281d10aa","observation_id":"07dd318f-d26b-4f93-9c70-53b133856ade","resolution":{"observed_at":"2026-05-10T18:50:44.407912Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13280","last_updated":"2023-09-07T11:46:17Z","snapshot_observed_at":"2026-08-13T10:27:48.088578Z","submitted_at":"2023-08-25T10:02:26Z","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning","version":2},"cited_work":{"arxiv_id":"2308.13280","doi":"10.48550/arxiv.2308.13280","metadata_source":"arxiv_reference","pith_arxiv_id":"2308.13280","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mathilde Leuridan, James Hawkes, Simon Smart, Emanuele Danovaro, Martin Schultz, and Tiago Quintino","venue":"arXiv (Cornell University)","work_id":"136c6f62-53ee-4363-901e-120664e00f2a","year":2023},"citing_paper":{"arxiv_id":"2605.00850","last_updated":"2026-04-20T11:40:39Z","snapshot_observed_at":"2026-08-02T19:24:52.331112Z","submitted_at":"2026-04-20T11:40:39Z","title":"Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-10T03:22:44.396300Z"},"links":{"cited_paper":"/paper/2308.13280","citing_paper":"/paper/2605.00850"},"observation_digest":"sha256:72b26b8bcbb37a1230d1de0f3ff73b50f7b68fad8241655613d556b274c99323","observation_id":"702f1742-f841-48c1-8e9d-267181829e08","resolution":{"observed_at":"2026-05-11T12:31:06.418359Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13280","last_updated":"2023-09-07T11:46:17Z","snapshot_observed_at":"2026-08-13T10:27:48.088578Z","submitted_at":"2023-08-25T10:02:26Z","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning","version":2},"cited_work":{"arxiv_id":"2308.13280","doi":"10.48550/arxiv.2308.13280","metadata_source":"arxiv_reference","pith_arxiv_id":"2308.13280","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mathilde Leuridan, James Hawkes, Simon Smart, Emanuele Danovaro, Martin Schultz, and Tiago Quintino","venue":"arXiv (Cornell University)","work_id":"136c6f62-53ee-4363-901e-120664e00f2a","year":2023},"citing_paper":{"arxiv_id":"2605.28851","last_updated":"2026-05-16T20:37:05Z","snapshot_observed_at":"2026-08-14T07:23:43.570299Z","submitted_at":"2026-05-16T20:37:05Z","title":"Towards a Foundation Model for the Martian Atmosphere","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-06-30T19:01:31.373340Z"},"links":{"cited_paper":"/paper/2308.13280","citing_paper":"/paper/2605.28851"},"observation_digest":"sha256:2af72a63c8790e9cf7bc5c95b8d0c065f9728cf945bf6f139d325511242ad3d3","observation_id":"e9d8eefc-f03c-462c-a0fc-d304a9f9030d","resolution":{"observed_at":"2026-06-30T19:05:00.834018Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.13280","last_updated":"2023-09-07T11:46:17Z","snapshot_observed_at":"2026-08-13T10:27:48.088578Z","submitted_at":"2023-08-25T10:02:26Z","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning","version":2},"cited_work":{"arxiv_id":"2308.13280","doi":"10.48550/arxiv.2308.13280","metadata_source":"arxiv_reference","pith_arxiv_id":"2308.13280","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Mathilde Leuridan, James Hawkes, Simon Smart, Emanuele Danovaro, Martin Schultz, and Tiago Quintino","venue":"arXiv (Cornell University)","work_id":"136c6f62-53ee-4363-901e-120664e00f2a","year":2023},"citing_paper":{"arxiv_id":"2606.25076","last_updated":"2026-06-23T18:33:33Z","snapshot_observed_at":"2026-08-14T06:58:32.063963Z","submitted_at":"2026-06-23T18:33:33Z","title":"Machine learning is revolutionizing weather forecasting -- the next step is a change in how we work","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-06-25T21:31:02.801031Z"},"links":{"cited_paper":"/paper/2308.13280","citing_paper":"/paper/2606.25076"},"observation_digest":"sha256:a4168adec7698d2aa0c12032f1aec3caa09d33787ea77ef03c34fd3f4669e8ef","observation_id":"fc46fa44-0092-472c-8b08-be2286d48761","resolution":{"observed_at":"2026-07-04T19:20:06.562497Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2308.13280/citation-record","integrity":"/paper/2308.13280/integrity","json":"/paper/2308.13280/citation-record.json","paper":"/paper/2308.13280"},"outbound":[],"paper":{"arxiv_id":"2308.13280","last_updated":"2023-09-07T11:46:17Z","latest_version":2,"primary_category":"physics.ao-ph","snapshot_observed_at":"2026-08-13T10:27:48.088578Z","submitted_at":"2023-08-25T10:02:26Z","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2308.13280."}