{"as_of":"2026-08-13T09:45:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a5c4be093088351c5d356e08ac8920213f5f4303373fd654bb52459849b46102","coverage":[{"denominator":46,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":46,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T14:19:21.566559Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-08T16:38:36.296404Z","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":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"cited_work":{"arxiv_id":"2411.16728","doi":"10.48550/arxiv.2411.16728","metadata_source":"arxiv_reference","pith_arxiv_id":"2411.16728","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":null,"venue":"arXiv (Cornell University)","work_id":"116caf09-c087-477b-8dd2-36b3f9e8ba5b","year":2024},"citing_paper":{"arxiv_id":"2605.05255","last_updated":"2026-05-05T23:52:32Z","snapshot_observed_at":"2026-07-06T23:17:52.987860Z","submitted_at":"2026-05-05T23:52:32Z","title":"Prediction of Drought and Flash Drought in Africa at the Seasonal-to-Subseasonal Scale using the Community Research Earth Digital Intelligence Twin Framework","version":1},"reference_index":171,"source":"arxiv_source","source_observed_at":"2026-05-08T16:38:36.296404Z"},"links":{"cited_paper":"/paper/2411.16728","citing_paper":"/paper/2605.05255"},"observation_digest":"sha256:9c147fcb66739235e05df574e7780b3680f140b19045980ee520d9b87f0f7410","observation_id":"575e9154-0cc1-4e95-881a-fe0ce2aa8d66","resolution":{"observed_at":"2026-05-08T16:53:29.703567Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.16728/citation-record","integrity":"/paper/2411.16728/integrity","json":"/paper/2411.16728/citation-record.json","paper":"/paper/2411.16728"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.503212Z","title":"https://damo.alibaba","venue":null,"work_id":"5fa44d15-5b1b-4ede-8184-54ff9a50c301","year":2024},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.335732Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:f59ad64a0e840eefec41b4e1ea7dd83b65d37a38e4ccda8c4733af3b2f6927da","observation_id":"32c67a25-efb6-4193-9624-fef574f51d59","resolution":{"observed_at":"2026-08-12T14:19:22.508238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.485930Z","title":"Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks","venue":null,"work_id":"fc65d758-baeb-4766-958e-13506d3bfecc","year":null},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.342218Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:8292cccdd0cb9194c7de257e09367ee4c50f04c0c7c4c0305bb618d1e2d0cf9a","observation_id":"b860bef0-b53e-4e39-ad07-25d02cec7feb","resolution":{"observed_at":"2026-08-12T14:19:22.491563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.470216Z","title":"The quiet revolution of numerical weather prediction","venue":null,"work_id":"181b1d3e-89b2-45b2-b3a9-c1223cdd6cc6","year":2015},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.347564Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:d86607cbf20ecf251e2fc84782578b7706bd4707974d6ac35fddb6c9f62c289d","observation_id":"f20d92b4-0501-4151-9202-cb1496196d87","resolution":{"observed_at":"2026-08-12T14:19:22.475467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.453415Z","title":"Curriculum learning","venue":null,"work_id":"d56dcce3-356b-4354-a711-37d0b0baa680","year":2009},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.352860Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:c916fb6986dcd68563c861156e4f80ca38110a9fab3be3a01eab51016d3936d9","observation_id":"d1ea305c-5317-4190-9886-fbc7be0205e8","resolution":{"observed_at":"2026-08-12T14:19:22.459217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.02556","last_updated":"2022-11-03T17:19:43Z","snapshot_observed_at":"2026-08-11T16:41:57.519141Z","submitted_at":"2022-11-03T17:19:43Z","title":"Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.02556","snapshot_observed_at":"2026-08-12T14:19:21.358324Z","title":"Pangu-weather: A 3d high-resolution model for fast and accurate global weather forecast","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.358324Z"},"links":{"cited_paper":"/paper/2211.02556","citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:4b758e9abbbde118cfbfa7f26ecb7d6c75fe0e363f0cd5b3c1777845c7c7ca22","observation_id":"b4c3b1be-f6c4-4b19-88ed-3d5e93cc8aef","resolution":{"observed_at":"2026-08-12T14:19:21.358324Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.13063","last_updated":"2024-11-21T20:14:58Z","snapshot_observed_at":"2026-08-13T00:03:44.865059Z","submitted_at":"2024-05-20T14:45:18Z","title":"A Foundation Model for the Earth System","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.13063","snapshot_observed_at":"2026-08-12T14:19:21.363893Z","title":"Bruinsma, Ana Lucic, Megan Stanley, Johannes Brandstetter, Patrick Garvan, Maik Riechert, Jonathan A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.363893Z"},"links":{"cited_paper":"/paper/2405.13063","citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:ee940c3e3afb3f4ceb2ccf03ee3af923642a0ea2228b53212faaa3b17eeceb21","observation_id":"e93b0115-8929-49cb-8dc1-b7592350e1ec","resolution":{"observed_at":"2026-08-12T14:19:21.363893Z","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":"2026-08-12T14:19:22.435509Z","title":"Spherical fourier neural operators: Learning stable dynamics on the sphere","venue":null,"work_id":"52855ad7-2966-48e4-9328-b12cb956fa70","year":2023},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.370105Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:a77418eeb3fedac480b8bdcf65646d21e6a8b1573da7776c87f643203a269112","observation_id":"4036ca90-5280-478b-923b-d1f6a9a06d38","resolution":{"observed_at":"2026-08-12T14:19:22.440662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.419233Z","title":null,"venue":null,"work_id":"0e34f2c1-802b-4ca0-9480-d3ee5ac93a94","year":2023},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.375391Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:951be1ad16222861e84ea52b3b9e109a0c3fbc965670d46ba999f42a472d7221","observation_id":"e761ea0f-e611-417f-9725-94b6de75f6ba","resolution":{"observed_at":"2026-08-12T14:19:22.424130Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T14:19:21.380668Z","title":"Fengwu: Pushing the skillful global medium-range weather forecast beyond 10 days lead","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.380668Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:91e1c8064ab303a5821cae7d716bc1a56af413b25e242e413a8664101b82ea2a","observation_id":"d03454f7-fdc9-4c00-a260-78c50b7ad154","resolution":{"observed_at":"2026-08-12T14:19:21.380668Z","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":"2026-08-12T14:19:22.402277Z","title":"Fuxi: a cascade machine learning forecasting system for 15-day global weather fore- cast","venue":null,"work_id":"1cde3acc-8379-4760-b54c-a2a7167613cd","year":2023},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.385608Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:94d3877c859540c61d20035d4a6099636ef707a46601ed4eadb32d97e8af0a42","observation_id":"1ee465b0-ffcc-49df-9f22-d42bd4934553","resolution":{"observed_at":"2026-08-12T14:19:22.408276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2312.09926","last_updated":"2024-07-05T08:23:00Z","snapshot_observed_at":"2026-08-13T05:00:50.581231Z","submitted_at":"2023-12-15T16:31:44Z","title":"FuXi-S2S: A machine learning model that outperforms conventional global subseasonal forecast models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.09926","snapshot_observed_at":"2026-08-12T14:19:21.390766Z","title":"Fuxi-s2s: An accurate machine learn- ing model for global subseasonal forecasts","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.390766Z"},"links":{"cited_paper":"/paper/2312.09926","citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:eed4398b6f1f366dca46c2ba361dd01979b0a9ba6fe87f0706cebb51a4279400","observation_id":"5b619cb9-d9ad-4e26-b81e-f75d8c55b60b","resolution":{"observed_at":"2026-08-12T14:19:21.390766Z","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":"2026-08-12T14:19:22.385805Z","title":"Fundamentals of numerical weather predic- tion","venue":null,"work_id":"ead22d73-e909-429b-9a4c-650be4cfd548","year":2011},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.396267Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:de28a0f1dc7beb6948c7394d6a329dc4de0f52265ab22ee932c7a552d607295b","observation_id":"2cec2625-1d3c-49fb-a9ae-780f31d655b1","resolution":{"observed_at":"2026-08-12T14:19:22.390882Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11929","last_updated":"2021-06-03T13:08:56Z","snapshot_observed_at":"2026-08-13T02:40:23.887636Z","submitted_at":"2020-10-22T17:55:59Z","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11929","snapshot_observed_at":"2026-08-12T14:19:21.401842Z","title":"An image is worth 16x16 words: Transformers for image recognition at scale","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.401842Z"},"links":{"cited_paper":"/paper/2010.11929","citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:8f8cbd1a698d59c6877ff8adf70bc16ad99c0dabd99fda91713f7d17bb4fb377","observation_id":"56ebdfdb-b27f-4131-90cf-29c74957b122","resolution":{"observed_at":"2026-08-12T14:19:21.401842Z","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":"2026-08-12T14:19:22.369615Z","title":"Siamese masked autoencoders","venue":null,"work_id":"a22af057-ae0c-490e-b55c-a8c22a3ca37a","year":2023},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.407754Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:15cd69a10ca325e720bb02bf5c588ecea06fb3b6a39ebcd7eed3d900823719c0","observation_id":"ba20c02b-cefe-4e4e-8b8e-edd6d61f203f","resolution":{"observed_at":"2026-08-12T14:19:22.374703Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T14:19:21.412920Z","title":"The era5 global reanalysis","venue":null,"work_id":null,"year":1999},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.412920Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:ba6c82247185589716f8667179dd7ce0d4a54af37fdb962345cc7cc2e88d5871","observation_id":"d66c4078-79e6-42f0-9fbb-9a49c61706ef","resolution":{"observed_at":"2026-08-12T14:19:21.412920Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.04406","last_updated":"2023-10-27T09:30:19Z","snapshot_observed_at":"2026-08-09T09:18:07.596557Z","submitted_at":"2023-06-07T13:04:34Z","title":"Generalized Teacher Forcing for Learning Chaotic Dynamics","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.04406","snapshot_observed_at":"2026-08-12T14:19:21.417581Z","title":"Generalized teacher forcing for learning chaotic dynamics","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.417581Z"},"links":{"cited_paper":"/paper/2306.04406","citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:447116669c2417f99de0d896ffff8f25935708098dbf4720affc0c35d57e011d","observation_id":"23eeb563-7563-46a4-88b8-0e0234ace5c6","resolution":{"observed_at":"2026-08-12T14:19:21.417581Z","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":"2026-08-12T14:19:22.342986Z","title":"Parameter-efficient transfer learning for nlp","venue":null,"work_id":"eb1ddd94-832a-43ce-856a-f057c41e8c87","year":2019},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.423506Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:4d8e070f16cf9fb565f6da7be26407d264ba02aa8dd9a993c130c2b1701d47f5","observation_id":"4c9ddab9-6a85-4c61-9b1b-36f88850dcde","resolution":{"observed_at":"2026-08-12T14:19:22.348436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.07987","last_updated":"2024-07-25T09:33:50Z","snapshot_observed_at":"2026-08-06T11:02:06.607024Z","submitted_at":"2024-05-13T17:58:30Z","title":"The Platonic Representation Hypothesis","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.07987","snapshot_observed_at":"2026-08-12T14:19:21.429398Z","title":"The platonic representation hypothesis","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.429398Z"},"links":{"cited_paper":"/paper/2405.07987","citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:c98decb967fb8ae33f96d3c46390b230e6b43d097fa255b1314d6e62e5497990","observation_id":"f4e93099-d36c-440a-8631-5d35ede0003d","resolution":{"observed_at":"2026-08-12T14:19:21.429398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-12T14:19:21.434485Z","title":"Auto-encoding variational bayes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.434485Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:028e2f397f698ed8a49116f162ea84c1f4acf4f2ce3702e1a96273636f582584","observation_id":"c62a4d72-c77e-4053-aa63-69f93e7b60d5","resolution":{"observed_at":"2026-08-12T14:19:21.434485Z","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":"2026-08-12T14:19:22.326078Z","title":"Similarity of neural network represen- tations revisited","venue":null,"work_id":"39465721-1c3b-49c1-9454-86fb6cdebdf8","year":2019},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.439271Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:a06c9c5af90e0f440ae7e02e8f434df3e85a0b9ad02409ce140583982809de54","observation_id":"ce2fc628-240e-4259-8aef-269b59755a09","resolution":{"observed_at":"2026-08-12T14:19:22.331463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.310214Z","title":"Learning skillful medium-range global weather forecasting","venue":null,"work_id":"353010b3-f688-4cae-b30c-888be2125ec5","year":2023},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.444109Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:8751427adbcc681611a4f5c38e78d6784a3cc1adb1235da7e1cd5836db5970eb","observation_id":"e9366d4e-6b98-4bc4-bd48-fd3f2926fad8","resolution":{"observed_at":"2026-08-12T14:19:22.315186Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.294079Z","title":"Analysis methods for numerical weather prediction","venue":null,"work_id":"29d28e22-868d-490d-82d1-6da4621dc23d","year":1986},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.450177Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:c78b8fbae53f3d9b7345f465e3ee02406ace95207b8b0306f7afb99cfc3b90fb","observation_id":"8813b66d-3595-4826-a264-b404c7abe0da","resolution":{"observed_at":"2026-08-12T14:19:22.299534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.276675Z","title":"Deterministic nonperiodic flow","venue":null,"work_id":"198e0aaf-299c-4723-b42b-46217837560a","year":1963},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.455210Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:54765105101f8ff5f09112d0d2dc0877b4d3b03d02c061051fb961979ade611c","observation_id":"db216036-faff-4730-a193-3566c123a433","resolution":{"observed_at":"2026-08-12T14:19:22.282776Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.258803Z","title":"On the difficulty of learning chaotic dynamics with rnns","venue":null,"work_id":"b031a60d-fbc3-4945-b747-04dee8d62f70","year":2022},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.459877Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:692e7c1b6dcd63593a95fcbc1c30ede742df0dc280cd11c51bc1dc97d48cbed1","observation_id":"15e061a0-8a56-43a3-bc32-86f10a6d4c8e","resolution":{"observed_at":"2026-08-12T14:19:22.263719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.242350Z","title":"Adaptive bias correction for im- proved subseasonal forecasting","venue":null,"work_id":"0cf9357a-836c-4d13-a3fd-aa33f5b7e6ba","year":2023},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.464690Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:11daf4df2d3d8fd5882cd182825d031ee97637d80614b6c23a721bdc31933817","observation_id":"0a4f412e-9483-44a4-8641-6b8adf1efc0b","resolution":{"observed_at":"2026-08-12T14:19:22.247864Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00712","last_updated":"2024-11-20T19:57:16Z","snapshot_observed_at":"2026-08-13T04:29:08.532882Z","submitted_at":"2024-02-01T16:07:12Z","title":"ChaosBench: A Multi-Channel, Physics-Based Benchmark for Subseasonal-to-Seasonal Climate Prediction","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.00712","snapshot_observed_at":"2026-08-12T14:19:21.469543Z","title":"Chaosbench: A multi-channel, physics-based benchmark for subseasonal-to-seasonal climate prediction","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.469543Z"},"links":{"cited_paper":"/paper/2402.00712","citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:c3bbd0cdfd0eb0d13cd3bf1014ef96e3f4562618c604a2541467377cbc74177d","observation_id":"fb9bfb56-139d-455c-a957-b6877c56146d","resolution":{"observed_at":"2026-08-12T14:19:21.469543Z","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":"2026-08-12T14:19:22.226017Z","title":"Gupta, and Aditya Grover","venue":null,"work_id":"97803e6d-61d1-4277-8f48-26d478793da2","year":2023},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.474516Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:77cafa34eb940c4efa1076ded1913c4ca0cf19ffdb4b7412ffc96957b08a4333","observation_id":"be229320-21df-4ae5-a3b8-9e6c9fbe4e95","resolution":{"observed_at":"2026-08-12T14:19:22.231178Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.11214","last_updated":"2022-02-22T22:19:35Z","snapshot_observed_at":"2026-08-02T21:09:07.860947Z","submitted_at":"2022-02-22T22:19:35Z","title":"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.11214","snapshot_observed_at":"2026-08-12T14:19:21.478996Z","title":"Fourcastnet: A global data-driven high- resolution weather model using adaptive fourier neural op- erators","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.478996Z"},"links":{"cited_paper":"/paper/2202.11214","citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:922283de37ebc8ed6e25efddf2e26a443f321634e3196a1bb05eeb29bb69006b","observation_id":"93bfc50c-8462-4597-a75a-17d596a5cbbe","resolution":{"observed_at":"2026-08-12T14:19:21.478996Z","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":"2026-08-12T14:19:22.210803Z","title":"Pendergrass, Gerald A","venue":null,"work_id":"327740d5-9380-4dba-8894-528a5bdbb1fe","year":2020},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.484255Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:c8f71541829b02401915216a72ce2a3af2627090035da1363dad7e389598f8d1","observation_id":"a0323edf-4e5e-43f7-81bb-0480ef99d660","resolution":{"observed_at":"2026-08-12T14:19:22.215725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.193971Z","title":"The role of model and initial condition error in numerical weather forecasting in- vestigated with an observing system simulation experiment","venue":null,"work_id":"1ca44fe7-5b1f-4c14-b4eb-37c2b1e0f9f1","year":2013},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.489406Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:fa59e2f4d3861c6e414387bff125af8525ac063d7c8c569da95f1be260099bd5","observation_id":"319382a1-4840-4446-bae7-94ca80c36b16","resolution":{"observed_at":"2026-08-12T14:19:22.199058Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T14:19:21.494161Z","title":"U- net: Convolutional networks for biomedical image segmen- tation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.494161Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:7e6ed8e3e2c4a2e33a4f54537bcdca3182cee60f49a3cc58b0464ffe93ad6e42","observation_id":"d226d039-3f16-42f7-88ba-35717d0de333","resolution":{"observed_at":"2026-08-12T14:19:21.494161Z","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":"2026-08-12T14:19:21.499146Z","title":"Learning representations by back-propagating er- rors","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.499146Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:58ac21aee8f129433e17d9e33fee77c42fb648f52b16a9de8a0f17057506a61c","observation_id":"80b2f9b4-68dd-4994-9ad5-d177e9e519d3","resolution":{"observed_at":"2026-08-12T14:19:21.499146Z","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":"2026-08-12T14:19:22.158313Z","title":"The ncep climate forecast system version 2","venue":null,"work_id":"a0aace35-c5fe-4860-90c9-ce4e03a1fd35","year":2014},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.503834Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:b1777203ea4406333f022b6360fa8fe3a6acdcf0ce2b3db2b7f6ee76ec65a07e","observation_id":"43e43798-6292-4db4-90df-3aec434f9157","resolution":{"observed_at":"2026-08-12T14:19:22.163219Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.142947Z","title":"Lamb, Yu Huang, and Pierre Gen- tine","venue":null,"work_id":"04e85bb8-cf57-4232-b2df-90e942c2f9e3","year":2023},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.508516Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:a963e37fcfb1cf216f0fd0238e4cab35a21227dd7204529b526180e00df6e412","observation_id":"fb2a5149-deba-454e-b200-effb97e1a840","resolution":{"observed_at":"2026-08-12T14:19:22.148388Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.10024","last_updated":"2024-04-15T06:38:21Z","snapshot_observed_at":"2026-08-13T00:30:13.663391Z","submitted_at":"2024-04-15T06:38:21Z","title":"ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.10024","snapshot_observed_at":"2026-08-12T14:19:21.513215Z","title":"Climode: Climate and weather forecasting with physics-informed neu- ral odes","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.513215Z"},"links":{"cited_paper":"/paper/2404.10024","citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:47e86c06614f1282bd3763ff32f5abecc09bd5009d9157cc6856c0aa287b0f84","observation_id":"0b39a050-b04d-4f01-8bbe-0eeb94791315","resolution":{"observed_at":"2026-08-12T14:19:21.513215Z","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":"2026-08-12T14:19:22.127706Z","title":"Evolution of ecmwf sub-seasonal forecast skill scores","venue":null,"work_id":"2890c93c-d793-4b6a-a502-56ea1c9714b2","year":2014},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.518707Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:0169605d9d81bf30e3b9be501983e268dc4cfa902f7182a8abb057401ff90456","observation_id":"6b877285-c4b7-4eb5-9479-3199aeaefd5f","resolution":{"observed_at":"2026-08-12T14:19:22.132655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.110806Z","title":"The sub-seasonal to seasonal prediction project (s2s) and the prediction of ex- treme events","venue":null,"work_id":"62678980-e422-4c38-b91f-d5f1c03526a8","year":null},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.523656Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:459d3480e327511ca46231093ca2f2c4fe9ba80de795477ee5ff339a9757b188","observation_id":"ed2d29a7-3646-471f-8d2b-4163c9880958","resolution":{"observed_at":"2026-08-12T14:19:22.116089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.095370Z","title":"Subseasonal to seasonal prediction project: Bridging the gap between weather and climate","venue":null,"work_id":"e772ab61-b756-4bf5-9c7c-a64662cddaf7","year":2012},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.528474Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:3071d24e8bd7fa9447fd52398967ea39ff8b6cb8cd7a6bce5da3049592f63e6f","observation_id":"de527eb8-0589-4c49-954e-c549896f07ed","resolution":{"observed_at":"2026-08-12T14:19:22.100492Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.079627Z","title":"Backpropagation through time: what it does and how to do it","venue":null,"work_id":"20701f47-c37b-479b-8890-56fc49384641","year":1990},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.533466Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:24cf94489a8a5f9d8104147d47bb804fed9eb7c474ee9fd652e956b6ea58813c","observation_id":"7c22f492-4e01-4350-a5a9-16dbc5917466","resolution":{"observed_at":"2026-08-12T14:19:22.084886Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.062515Z","title":"An all-season real-time multivariate mjo index: Development of an in- dex for monitoring and prediction","venue":null,"work_id":"480efe07-8077-4d75-811e-5a11f4954d9c","year":1917},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.538086Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:024c58020d054d5e24a637f82dbe2d3259b45a2fc6dd31981da59bcc20758180","observation_id":"b5cd732c-b008-470a-847f-6f610a6d7b2b","resolution":{"observed_at":"2026-08-12T14:19:22.068369Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.045426Z","title":"Potential applications of subseasonal-to-seasonal (s2s) predictions","venue":null,"work_id":"90bd1c57-2986-42a9-ade6-16882dec7b57","year":2017},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.542940Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:adf7d3f6bc3bd0b7b02c7d592ff309d31b45e8f72edf45633e0ef690efff39ec","observation_id":"9c34418d-447f-4168-8be5-3d8c67156268","resolution":{"observed_at":"2026-08-12T14:19:22.050648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.027955Z","title":"The met office global coupled model 2.0 (gc2) con- figuration","venue":null,"work_id":"e4c20624-3552-4a57-95f7-4aadd5dd4e0d","year":2015},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.547691Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:0d98560531b8ef9dc48aec6240b7cd6fb18112143c3cce3438aa39558a45aef9","observation_id":"1abead12-1fa4-4445-b35c-d9906da4fe76","resolution":{"observed_at":"2026-08-12T14:19:22.032690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:22.011674Z","title":"The beijing climate center climate system model (bcc-csm): The main progress from cmip5 to cmip6","venue":null,"work_id":"8d8560d5-ae8e-41eb-98e3-8032fdc15874","year":2019},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.552448Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:d5180c7bf8fe3edab984d4b3b4b288056a8338f8338d5571caae69c27ddc59a9","observation_id":"35215364-290b-444e-b2d9-6fa4355853a0","resolution":{"observed_at":"2026-08-12T14:19:22.016881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:21.995725Z","title":"Estimating the uncertainty in a regional climate model related to initial and lateral boundary conditions","venue":null,"work_id":"7bcc1ba0-be76-4900-9dc3-c82436ba7d9f","year":2005},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.556842Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:4c64f771459c311cb7fba9f227488f9e4d94668a4c28b416a23eeda0563b4055","observation_id":"fc037171-6cb4-47d6-adc3-87032beeee59","resolution":{"observed_at":"2026-08-12T14:19:22.000737Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:21.980110Z","title":"Vargas Zeppetello, David S","venue":null,"work_id":"5c3427f8-3af7-473c-b4bf-a9463b88a0fa","year":2022},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.562034Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:879866ddbfc1871702f1b9322a967c6f60fc64c51b96a26ae1b3ce0850643d49","observation_id":"a81e0b13-afa0-46aa-aeed-f02d48fb4ef2","resolution":{"observed_at":"2026-08-12T14:19:21.984982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T14:19:21.961773Z","title":"Gradient descent with identity initialization efficiently learns positive definite linear trans- formations by deep residual networks","venue":null,"work_id":"59dd750b-fc26-498e-a55c-469434e5cc6a","year":null},"citing_paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T14:19:21.566559Z"},"links":{"citing_paper":"/paper/2411.16728"},"observation_digest":"sha256:d0f29a9da37f8027652c8fc06a571c13fd6fac2d17ca8ac3497f8334ffb3b12c","observation_id":"98ef0209-7184-48c2-825e-53672569f936","resolution":{"observed_at":"2026-08-12T14:19:21.968608Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.16728","last_updated":"2024-11-23T08:01:54Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T06:42:25.069912Z","submitted_at":"2024-11-23T08:01:54Z","title":"Maximizing the Impact of Deep Learning on Subseasonal-to-Seasonal Climate Forecasting: The Essential Role of Optimization"},"reference_resolution":{"displayed":46,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":31},"total_outbound_references":46},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2411.16728."}