{"as_of":"2026-08-14T19:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0ab943e58673853807df72771a01ebcba05bfb3fa48f646c81a0f4849591e24d","coverage":[{"denominator":28,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":28,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T00:42:56.310253Z","state":"measured"},{"denominator":28,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":28,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+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/2608.00352/citation-record","integrity":"/paper/2608.00352/integrity","json":"/paper/2608.00352/citation-record.json","paper":"/paper/2608.00352"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-04T00:42:53.583520Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:53.583520Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:a7cea04b64d7168f42faf884d1899a6ba531a17e644ff0a1bf22a663b7b62ac2","observation_id":"b7ccac13-cfa5-47e8-a983-870aaa9de6f5","resolution":{"observed_at":"2026-08-04T00:42:53.583520Z","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":"2026-08-04T00:42:53.665832Z","title":"and Kubaryk, A","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:53.665832Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:d09a866ed34b8f70a13d0d8f0a462a84898af468aa015949f9b7b5144208a291","observation_id":"9ec80f29-2196-418c-a9f6-76b6c7372a6f","resolution":{"observed_at":"2026-08-04T00:42:53.665832Z","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":"2026-08-04T00:42:53.805645Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:53.805645Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:a005d493e8f66647c47a2edbd1e0d5ffe810de3297a436f659d25ef13e386d64","observation_id":"941dde6e-8722-4321-8ccd-01a9e07677b5","resolution":{"observed_at":"2026-08-04T00:42:53.805645Z","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":"2026-08-04T00:42:53.878116Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:53.878116Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:c2f331c5a93b81d1916da21bcfc31096573ab79cc155db1320b86ced4eab888f","observation_id":"edcb5b10-5eb0-4b6c-bd18-ce096a209b20","resolution":{"observed_at":"2026-08-04T00:42:53.878116Z","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":"2026-08-04T00:42:53.974963Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:53.974963Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:6d4a50fdfe2246887d7328566d831983dcf19e4fb656b5e6ab92cdc3ffaa0f63","observation_id":"db500250-61bb-400a-be38-1a0cdf3543a4","resolution":{"observed_at":"2026-08-04T00:42:53.974963Z","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":"2026-08-04T00:42:54.086284Z","title":"and Rougier, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:54.086284Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:b461255dbf69b12a0f09d0b72e0c9a0b2d5ddb905abe632c2b29b32bef4380ec","observation_id":"2ec1956a-3a95-4a47-ae48-b2413773b8f7","resolution":{"observed_at":"2026-08-04T00:42:54.086284Z","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":"2026-08-04T00:42:54.154226Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:54.154226Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:7119f9bbd677902d2a1bfbb11db9d5de083ffa4ad01230b4f1326e0a5dc0d1d1","observation_id":"9ecdf9f7-f698-4ffe-9447-44408adee33e","resolution":{"observed_at":"2026-08-04T00:42:54.154226Z","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":"2026-08-04T00:42:54.291786Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:54.291786Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:4ca269f9191ce8fa103c3d57c0f4f5355f69663479dec77b20585433d3d8a2b1","observation_id":"d0661540-d479-47a4-9858-422c95b7408d","resolution":{"observed_at":"2026-08-04T00:42:54.291786Z","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":"2026-08-04T00:42:54.410457Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:54.410457Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:f95f6138e1eea71b3616a1a27bdc5d1c8c2fad4ba2c3b50887296477ca19db57","observation_id":"06c65f47-9042-4d46-90e4-0f71dea74d2e","resolution":{"observed_at":"2026-08-04T00:42:54.410457Z","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":"2026-08-04T00:42:54.488607Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:54.488607Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:d325530b9c1200e4df4047ae6251ebc7a8de94664f5e6e7b0b80b4e1ef795d10","observation_id":"e675fa28-bb16-46cb-9567-ea003575ebc7","resolution":{"observed_at":"2026-08-04T00:42:54.488607Z","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":"2026-08-04T00:42:54.561286Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:54.561286Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:e6bfb178d993231067672ad491350ea527e474788c19c2ecda62919bcae32bbc","observation_id":"2ded4d63-4a6f-4b69-8612-40edcf95bcee","resolution":{"observed_at":"2026-08-04T00:42:54.561286Z","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":"2026-08-04T00:42:54.658940Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:54.658940Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:69d80447de680ce498b1cf5cf565c06095c145d015e930ba7b5f1dba26295168","observation_id":"cb51e922-2e2c-4461-b0bf-e1997ccaa88a","resolution":{"observed_at":"2026-08-04T00:42:54.658940Z","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":"2026-08-04T00:42:54.784102Z","title":"Acta Astronautica , volume =","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:54.784102Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:0da74cb086eae23d36642abf5512aa163feac05c4d0a909170b0f38dc6fd7019","observation_id":"434e86c9-a417-4915-aea9-77f6d1fda2ae","resolution":{"observed_at":"2026-08-04T00:42:54.784102Z","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":"2026-08-04T00:42:54.888142Z","title":"and Lei, J","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:54.888142Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:853c6585f38dd83cfc5ba0d17af2886a8c7dd230318058463404232db9fbd55a","observation_id":"5d0f4ae1-84e5-4ff9-91cd-16e68004d3bf","resolution":{"observed_at":"2026-08-04T00:42:54.888142Z","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":"2026-08-04T00:42:55.003804Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:55.003804Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:08d4903001e854bd806c45ed5cfa5c083e2fc7bcab488726c2b7a48a5c7c30d2","observation_id":"e44a94c8-edfa-43a9-b89a-b67f379e3383","resolution":{"observed_at":"2026-08-04T00:42:55.003804Z","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":"2026-08-04T00:42:55.108663Z","title":"and Liu, L","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:55.108663Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:d590c759b45e165413ea8a1903259feb539f5a3eb9d8ca3c07e00cd0b9f457e2","observation_id":"fd9eecd1-632b-43f9-850c-4ae140842a83","resolution":{"observed_at":"2026-08-04T00:42:55.108663Z","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":"2026-08-04T00:42:55.278043Z","title":"and Bai, X","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:55.278043Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:66e1d1eb96f52abe11803d6e3452e72b6dad533edadf7187960a3ceaee10e554","observation_id":"fc664441-166c-4759-a071-80a68b5774bf","resolution":{"observed_at":"2026-08-04T00:42:55.278043Z","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":"2026-08-04T00:42:55.373163Z","title":"and Kosary, M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:55.373163Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:f4765a8d24dd3e37f8dc4828451d2bd4717e2b8773186d13e22323e6dde2c281","observation_id":"8ab08b0e-4439-47a3-ba39-eeb38ceec263","resolution":{"observed_at":"2026-08-04T00:42:55.373163Z","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":"2026-08-04T00:42:55.510030Z","title":"and Xiong, C","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:55.510030Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:b86d3ffbbfd52109f3bae7595ac6db8ce133232556d41f7ea82263fb2c86c038","observation_id":"c26e6f88-f6cb-4aa5-a647-97158a86e93e","resolution":{"observed_at":"2026-08-04T00:42:55.510030Z","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":"2026-08-04T00:42:55.658327Z","title":"and Schwarting, W","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:55.658327Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:a0fc94a7eef030521b3a0adb36a20d7687a9837d20d1d7b4fb2cd0e81388bc3d","observation_id":"1faf0c16-ebd7-44ea-82f7-ebff3ecc744a","resolution":{"observed_at":"2026-08-04T00:42:55.658327Z","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":"2026-08-04T00:42:55.773941Z","title":"and Peng, H","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:55.773941Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:6d34454f0204ca489586755ad53051cfa97ac0493a837438f71e521b7554d016","observation_id":"c399e014-ac77-420a-b431-5d64111e22bc","resolution":{"observed_at":"2026-08-04T00:42:55.773941Z","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":"2026-08-04T00:42:55.793331Z","title":"and Doostan, A","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:55.793331Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:f3a1d0f1095d72af9299251769b2582ef92a919850920a9f582ca6941b7d80ed","observation_id":"41da307b-5b9d-40fd-8598-6e3e7e055371","resolution":{"observed_at":"2026-08-04T00:42:55.793331Z","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":"2026-08-04T00:42:55.817709Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:55.817709Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:ec130f0902746c98ea2a99dbbf3048a7e43b1ad85eab76240ff9c0aec48f5584","observation_id":"3b739165-a206-4897-8978-ddcd0e077d41","resolution":{"observed_at":"2026-08-04T00:42:55.817709Z","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":"2026-08-04T00:42:55.902732Z","title":", year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:55.902732Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:6866887ce435b41a51fc276aa88c9bb56aa7351e1ae93e9718267c9e52925a71","observation_id":"1f173e9d-97fc-4afc-b030-edaae1dfcff2","resolution":{"observed_at":"2026-08-04T00:42:55.902732Z","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":"2026-08-04T00:42:56.020417Z","title":", year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:56.020417Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:9d1f194fdfd55a5d2e68895090653c014baa07ff32e8a2e08d9767a57a92cc27","observation_id":"0943dfd5-ce9d-4b20-9249-048545e9e600","resolution":{"observed_at":"2026-08-04T00:42:56.020417Z","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":"2026-08-04T00:42:56.111999Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:56.111999Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:9365d90ffabbca194009cef5fa8082dcd67fb4db4d8af9622c5ffe90877dd750","observation_id":"50871d99-0902-44ee-80ba-69292e6b4cfd","resolution":{"observed_at":"2026-08-04T00:42:56.111999Z","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":"10.5880/hpo.0003","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"2024 , title =","venue":"Publication Database GFZ (GFZ German Research Centre for Geosciences)","work_id":"9577131d-905f-416c-afd8-b8d531ca2142","year":2024},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:56.207319Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:b995eb3fc353649b3a77ffda79f7c8d1efb3b416ad2a0bd0ddfc132b063d8ddf","observation_id":"ddf1e5d3-4990-4cb0-8ba3-2bd328454117","resolution":{"observed_at":"2026-08-04T00:43:26.325321Z","resolver_source":"doi","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":"2026-08-08T02:38:07.990338+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-08T02:38:07.990338+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.20412490","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"2026 , publisher =","venue":"Open MIND","work_id":"56fdb4be-f516-49a5-b466-36eff01158ad","year":2026},"citing_paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-04T00:42:56.310253Z"},"links":{"citing_paper":"/paper/2608.00352"},"observation_digest":"sha256:13cef9f0b092eac8940cdb1e0da7084cb4cb72d9ff49b9dc84e7a65c6879e4ed","observation_id":"62ac3acb-07d2-4b50-ab6c-4318328d2935","resolution":{"observed_at":"2026-08-04T00:43:26.191899Z","resolver_source":"doi","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":"2026-08-08T02:38:08.072107+00:00","source":"crossref_status_cache"},{"observed_at":"2026-08-08T02:38:08.072107+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2608.00352","last_updated":"2026-07-31T23:43:50Z","latest_version":1,"primary_category":"physics.space-ph","snapshot_observed_at":"2026-08-14T19:05:52.217759Z","submitted_at":"2026-07-31T23:43:50Z","title":"A Machine-Learning-Based Global Thermospheric Density Forecasting Model"},"reference_resolution":{"displayed":28,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":26,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":28},"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 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2608.00352."}