{"as_of":"2026-08-14T09:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c849cbd32d4c6005ea54c5a823a47896750441d84a1fd6c3694f3441e4151b7e","coverage":[{"denominator":114,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T05:05:41.628144Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"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/2506.08929/citation-record","integrity":"/paper/2506.08929/integrity","json":"/paper/2506.08929/citation-record.json","paper":"/paper/2506.08929"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:41.202366Z","title":"Constraining ΩM and Dark Energy with Gamma-Ray Bursts","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.202366Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:93bd87e168cc074c2322989061d6032cdc3f20d33edf9bf01fc79451b0e7d6be","observation_id":"694b8794-5cf4-4d3e-9ef7-53709a2d59f7","resolution":{"observed_at":"2026-08-07T05:05:41.202366Z","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":"2026-08-07T05:05:41.206861Z","title":"A new method optimized to use gamma-ray bursts as cosmic rulers","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.206861Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:065b636d49dbe8e627bbb57400e6467e3a0d2e8dd56431dc10fa963924a4a156","observation_id":"2405c05e-e3a1-425d-b3e2-b9cb9534f3e0","resolution":{"observed_at":"2026-08-07T05:05:41.206861Z","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":"2026-08-07T05:05:41.210880Z","title":"Gamma-Ray Bursts: New Rulers to Measure the Universe","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.210880Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:6f70129fb530abbb74cf5427bb4bb6a0259edfd5b661bc47e9f704716fd9122f","observation_id":"3be8ca8a-347d-4131-844b-39a6b5c870dd","resolution":{"observed_at":"2026-08-07T05:05:41.210880Z","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":"2026-08-07T05:05:41.215153Z","title":"Gamma-ray bursts as standard candles to constrain the cosmological parameters","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.215153Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:e21b4be40be3932ffb2e16ea20310897732e60e385c3bb985dbb322953e0fac5","observation_id":"8dd75ce4-bbc1-4282-b969-5c8305a653f9","resolution":{"observed_at":"2026-08-07T05:05:41.215153Z","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":"2026-08-07T05:05:41.219526Z","title":"Calibration of gamma-ray burst luminosity indicators","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.219526Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:cb557f477cb7bc58aca610c2bfdf0a90eac26fcf9f6e7f876b4f58bdcc8fa380","observation_id":"aaefb809-9389-469a-b6c6-ee6b9843d5ea","resolution":{"observed_at":"2026-08-07T05:05:41.219526Z","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":"10.1086/368104","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Gamma-Ray Burst Hubble Diagram to z = 4.5","venue":"The Astrophysical Journal","work_id":"055ce736-1204-48df-aaf6-a89cc3933c97","year":2003},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.223896Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:1939d397b619d1e4651af9df9c496f5dcb587d3b01daf9bdc8d6ca9644440383","observation_id":"71a5bb47-9793-4fa3-a487-59e7087a93bc","resolution":{"observed_at":"2026-08-07T05:09:17.492970Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.228499Z","title":"The Hubble Diagram to Redshift >6 from 69 Gamma-Ray Bursts","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.228499Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:af6f3940e14c3ee32de955562b32af8cb3eb85ee0310b3823a2b4389de104738","observation_id":"1e4b35cb-7bcd-469b-b705-885836735eca","resolution":{"observed_at":"2026-08-07T05:05:41.228499Z","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":"2026-08-07T05:05:41.232512Z","title":"Constraining the cosmological parameters and transition redshift with gamma-ray bursts and supernovae","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.232512Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:eeff31af066c418701d215dd100b42580c0d1d5c799b00573f58813ad567af69","observation_id":"200a3bee-c583-49df-980c-f35ec3a27974","resolution":{"observed_at":"2026-08-07T05:05:41.232512Z","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":"2026-08-07T05:05:41.236837Z","title":"Can Gamma-Ray Bursts Be Used to Measure Cosmology? A Further Analysis","venue":null,"work_id":null,"year":2005},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.236837Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:9953adb9c4849aeef92cafd723f5d0328c18480788d8eb41b6173c8f48d11a90","observation_id":"7f4f1ab4-f221-434e-904d-985aadb5d382","resolution":{"observed_at":"2026-08-07T05:05:41.236837Z","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":"10.1086/590903","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"A Cosmology-Independent Calibration of Gamma-Ray Burst Luminosity Relations and the Hubble Diagram","venue":"The Astrophysical Journal","work_id":"a55b1b32-20b6-4c7e-af16-61ae94d517c9","year":2008},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.244862Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:622338f2875c829fa64aa37c0440790af000abd7a238b0f2979a4ae150e77173","observation_id":"fb922639-437e-41ea-97f0-9c036eea8255","resolution":{"observed_at":"2026-08-07T05:09:16.871042Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.248943Z","title":"Addressing the circularity problem in the Ep-Eiso correlation of gamma-ray bursts","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.248943Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:1e1d29d95da8605e92699450bfaf132a6d01631671cfdb7b3b66cec52f83d86e","observation_id":"82936d9c-d7c1-415c-8994-97fedb447ff1","resolution":{"observed_at":"2026-08-07T05:05:41.248943Z","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":"2026-08-07T05:05:41.252924Z","title":"Intrinsic spectra and energetics of BeppoSAX Gamma-Ray Bursts with known redshifts","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.252924Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:18e66b4b68315780c9ffeaa6b6bcf7a1039754e8ed83ff20fc8ab4f0a28f9284","observation_id":"5b92f4fc-8f0c-4082-837b-9a39c978edc2","resolution":{"observed_at":"2026-08-07T05:05:41.252924Z","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":"10.1016/j.nuclphysbps.2009.07.024","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Cosmography by GRBs: Gamma Ray Bursts as possible distance indicators","venue":"Nuclear Physics B - Proceedings Supplements","work_id":"8e92b224-8bf7-4104-9208-c5e861570cb8","year":2009},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.260891Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:d81061d216e985071e84b95161b03324827a75610c8e09672ba24ae963ba9075","observation_id":"e210c5f8-8f5a-48e1-ac30-7a884b81b4d9","resolution":{"observed_at":"2026-08-07T05:09:16.488634Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.264861Z","title":"Cosmology with gamma-ray bursts","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.264861Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:73db3bc886413a4823a8c9f89fb35a216f69e96d6d058ff76223dc25701bb89a","observation_id":"6fad474b-4191-4f19-8e4e-5e318080eda8","resolution":{"observed_at":"2026-08-07T05:05:41.264861Z","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":"2026-08-07T05:05:41.268698Z","title":"Cosmology with gamma-ray bursts","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.268698Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:02d2464ea8924fa482425cf6151b90edc145f9b7780c52014b82dcafdf788111","observation_id":"2f66b4f4-aa6f-4fe3-b208-509f1f8c9c84","resolution":{"observed_at":"2026-08-07T05:05:41.268698Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1103/physrevd.81.083518","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Constraints on cosmological models and reconstructing the acceleration history of the Universe with gamma-ray burst distance indicators","venue":"Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D, Particles, fields, gravitation, and cosmology","work_id":"880bfd8b-0278-43f3-8326-eab03c687f8d","year":2010},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.272699Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:a65e0edb5f6a9f9d5c6fcfb144d3e9725199846a08c6345add9febb7c16a0960","observation_id":"f80a1e12-1fb3-4177-9ac6-fd9cb3f9f089","resolution":{"observed_at":"2026-08-07T05:09:16.210827Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1051/0004-6361/201015919","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Constraints on the generalized Chaplygin gas model including gamma-ray bursts via a Markov Chain Monte Carlo approach","venue":"Astronomy and Astrophysics","work_id":"be693c84-ffb0-4859-938e-b6fd6a24d35a","year":2011},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.276964Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:c7c78608b5943b98bfaff28fd66eeedc11a0de49638172bc3e0f02c8bd391c32","observation_id":"90133ce2-cc24-41fd-8071-dc906a6e19d2","resolution":{"observed_at":"2026-08-07T05:09:15.872044Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1140/epjc/s10052-009-1086-z","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Reconstructing the cosmic expansion history up to redshift z = 6.29 with the calibrated gamma-ray bursts","venue":"The European Physical Journal C","work_id":"6e4ffb3a-3237-4360-b24f-63572cde93aa","year":2009},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.281347Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:58d06a98f69249cc469a1c2490eccff04bf6d0f9e3202e08efb14a157892eb13","observation_id":"743399bc-a702-4a3b-be63-25d001bc90cf","resolution":{"observed_at":"2026-08-07T05:09:15.655316Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/1475-7516/2010/08/020","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Observational constraints on cosmological models with the updated long gamma-ray bursts","venue":"Journal of Cosmology and Astroparticle Physics","work_id":"ee63696b-92e7-4607-9900-0653ac7372db","year":2010},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.285810Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:c5bb1ea4b43ca9d08ddfbceaf42dc5baa53e894467daac96149572516d5d1635","observation_id":"ca493109-1ba5-4c2c-834c-ab1c09f893ad","resolution":{"observed_at":"2026-08-07T05:09:15.465602Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/mnras/stac2925","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Intermediate redshift calibration of gamma-ray bursts and cosmic constraints in non-flat cosmology","venue":"arXiv (Cornell University)","work_id":"70634406-baa3-482d-9211-c675cddb0d10","year":2023},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.290080Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:8e5b5173f358a3347f024f86fbcce56bc8662506521b048e235f4005be2884ae","observation_id":"64c53580-e6bb-4764-b166-925180a0a395","resolution":{"observed_at":"2026-08-07T05:09:15.275461Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.294466Z","title":"Improving sampling and calibration of gamma-ray bursts as distance indicators","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.294466Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:4ffccae51d04ca89dbb498084e706f8394779ebfff96f042d064fe78c920dbab","observation_id":"3647a509-cd26-4e77-ba9d-577101e24a7f","resolution":{"observed_at":"2026-08-07T05:05:41.294466Z","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":"2026-08-07T05:05:41.298532Z","title":"Measuring dark energy with the Eiso - Ep correlation of gamma-ray bursts using model-independent methods","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.298532Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:441254fc7ec9635aef23cb5eba87904523f7e27b0183cf3c79471503850fef95","observation_id":"49798864-2f16-4f44-8da9-82675c3e2326","resolution":{"observed_at":"2026-08-07T05:05:41.298532Z","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":"10.3847/1538-4357/ab037b","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Calibration of Gamma-Ray Burst Luminosity Correlations Using Gravitational Waves as Standard Sirens","venue":"The Astrophysical Journal","work_id":"2cc8e726-b897-48e9-b1d7-97e592bcbebe","year":2019},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.302616Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:c9c62cea0cd07fba69fb7ff3c545635fdcf79754549cc52fd35f8037fe1a8de0","observation_id":"48956f5f-b38d-4f9c-b5f6-13e969c4a177","resolution":{"observed_at":"2026-08-07T05:05:42.955268Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1051/0004-6361/202140895","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Redshift evolution of the Amati relation: Calibrated results from the Hubble diagram of quasars at high redshifts","venue":"Astronomy and Astrophysics","work_id":"cb13f730-7346-43ce-884f-60ecfc0b5b4d","year":2021},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.306520Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:b360fb59b200845d8ccc35d2704183c2fd4b942666fbee419d72cc01a008685c","observation_id":"a9026174-fc62-4bf2-a48c-19b4460c46a6","resolution":{"observed_at":"2026-08-07T05:05:42.941424Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/galaxies12060069","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Calibration of Luminosity Correlations of Gamma-Ray Bursts Using Quasars","venue":"Galaxies","work_id":"e04f98bb-a0b3-4299-af9f-37f3b2b5ba3c","year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.310834Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:a946212ef13d2e920b848c233983d56eea64ace3085be490aa6834bcd83c7182","observation_id":"861bae90-58c8-46f1-993e-973bfc5ac152","resolution":{"observed_at":"2026-08-07T05:05:42.928243Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.315699Z","title":"Low redshift calibration of the Amati relation using galaxy clusters","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.315699Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:6e713f64f1711f8f8b1c683ea30f4907ba8eae8a3012f3a335daba003dee247d","observation_id":"e621fd5d-ce98-4863-a0e0-1f8739c22373","resolution":{"observed_at":"2026-08-07T05:05:41.315699Z","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":"2026-08-07T05:05:41.325240Z","title":"Measuring the cosmological parameters with the E p,i-Eiso correlation of gamma-ray bursts","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.325240Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:8491ab1c7b1169119418a566b76e688b655c1213e31b811ddca2c5e718a5c00f","observation_id":"1afd65e6-63bf-4acd-8c4d-8a556052ba16","resolution":{"observed_at":"2026-08-07T05:05:41.325240Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-07T05:05:41.329838Z","title":"Standardizing Platinum Dainotti-correlated gamma-ray bursts, and using them with standardized Amati-correlated gamma-ray bursts to constrain cosmological model parameters","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.329838Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:367f2faa678c324ad303ae2eb38c10dc9bc7fa27762c63e0549b07cd27f75e15","observation_id":"8765bd56-7781-4c70-a604-cceb01abb0bd","resolution":{"observed_at":"2026-08-07T05:05:41.329838Z","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":"2026-08-07T05:05:41.334368Z","title":"Standardizing Dainotti-correlated gamma-ray bursts, and using them with standardized Amati- correlated gamma-ray bursts to constrain cosmological model parameters","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.334368Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:188236efbc700774e52b4ae7049cf6d4772734614acd005a5ca9307f6982b1f1","observation_id":"c030a1f9-8f78-485c-a621-7f4824b3f202","resolution":{"observed_at":"2026-08-07T05:05:41.334368Z","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":"10.1093/mnras/staa2779","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Constraints on cosmological parameters from gamma-ray burst peak photon energy and bolometric fluence measurements and other data","venue":"Monthly Notices of the Royal Astronomical Society","work_id":"c44916ac-19b4-4f9e-8e4a-7636fa0acd9a","year":2020},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.339007Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:6b43cf561d8d50f2fad9032e882544bde2239a221e08ae16121ef124415fb9fc","observation_id":"cacf41f1-4af9-47b8-b3ff-7c27bf6bd566","resolution":{"observed_at":"2026-08-07T05:05:42.882960Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.343177Z","title":"Do gamma-ray burst measurements provide a useful test of cosmological models? J","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.343177Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:57eef4fadde51bfe19dcc156e828811bf5c3256505f129a3fe37ce28b240bce5","observation_id":"e0eaba1a-aa49-45eb-ad18-65e79ab52fba","resolution":{"observed_at":"2026-08-07T05:05:41.343177Z","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":"10.1093/mnras/stac1184","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Using lower redshift, non-CMB, data to constrain the Hubble constant and other cosmological parameters","venue":"arXiv (Cornell University)","work_id":"73efe0b3-6bbd-4ff3-930c-889121507acb","year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.347745Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:eecdc42d8b4214e0f0ef9fea996d9ea73757013e1faf1becdb423df380ad760a","observation_id":"ef09fabd-1650-4660-9a9e-b1c53cd3a800","resolution":{"observed_at":"2026-08-07T05:05:42.860658Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.352219Z","title":"Do high redshift QSOs and GRBs corroborate JWST?Phys","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.352219Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:928e7254050c42ba7eb1a0ed2cdf53f9cc3b7277087ffe5276d72abcc0cb38f3","observation_id":"ff356f38-7dff-4302-8d79-43eea1dfe676","resolution":{"observed_at":"2026-08-07T05:05:41.352219Z","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":"2026-08-07T05:05:41.356603Z","title":"Towards a new model-independent calibration of Gamma-Ray Bursts","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.356603Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:39b4761664671ff72853f13792f2855b425907d100cb779184edec65e1b9def3","observation_id":"f4b9e96f-7f4e-41af-bbe6-d98d9287fcfb","resolution":{"observed_at":"2026-08-07T05:05:41.356603Z","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":"10.1140/epjc/s10052-024-13304-5","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Detection of gamma-ray burst Amati relation based on Hubble data set and Pantheon+ samples","venue":"The European Physical Journal C","work_id":"17900393-16d0-4918-a5ec-39ea1fe2c7d4","year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.361008Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:6e1fce794c1c8638461226af02c809844f7f24d6dbd61be248e756dde9fd9e8a","observation_id":"d5c45fde-ead4-4427-8a6f-5a159dbc97fe","resolution":{"observed_at":"2026-08-07T05:05:42.838774Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.365623Z","title":"Measuring cosmological parameters with a luminosity-time correlation of gamma-ray bursts.Mon","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.365623Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:80e7380ab61e48480ca45d14884dd4439388fdbc495f45765acf103e94b1cacc","observation_id":"4b1614d4-3dc7-478f-b614-928ce85c93a1","resolution":{"observed_at":"2026-08-07T05:05:41.365623Z","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":"2026-08-07T05:05:41.370692Z","title":"Standardizing the gamma-ray burst as a standard candle and applying it to cosmological probes: Constraints on the two-component dark energy model","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.370692Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:5f3d6bbc25bd864bedc0dc41996f311c4291111cd9fc2151937c13fa1dd243dc","observation_id":"88be7fe7-3488-454b-bac3-dc93459f0a87","resolution":{"observed_at":"2026-08-07T05:05:41.370692Z","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":"10.3847/1538-4357/ac66d3","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"The Improved Amati Correlations from Gaussian Copula.Astrophys","venue":"The Astrophysical Journal","work_id":"3bd89821-f2ed-408c-af8f-ee02a7069357","year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.375278Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:6cc7e3672f12aaf278068acda9197cde7cbd9b45390fa683c1add391c3a2aa8e","observation_id":"628cae12-2c5f-48a6-8410-adc37d9fa447","resolution":{"observed_at":"2026-08-07T05:05:42.807696Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.379477Z","title":"Gamma-Ray Burst Constraints on Cosmological Models from the Improved Amati Correlation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.379477Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:75b1023f4527ba9b9496a2f73686bb749856079252099d65f66d4527fdec82f8","observation_id":"69660b49-e195-4836-9a11-65d0c39721c2","resolution":{"observed_at":"2026-08-07T05:05:41.379477Z","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":"10.48550/arxiv.2504.11132","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Testing Non-Coincident f (Q)-gravity with DESI DR2 BAO and GRBs","venue":"arXiv (Cornell University)","work_id":"4551d31c-80fd-4422-8923-7c9723c7d582","year":2025},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.383966Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:7fbf895370690bb179575cf531d08c8eedc5e6aa149bf729963ca2edcd10b207","observation_id":"d36ecf2e-8099-4e80-8d6b-ed64ac6a207a","resolution":{"observed_at":"2026-08-07T05:05:42.785114Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.388864Z","title":"Radio Plateaus in Gamma-Ray Burst Afterglows and Their Application in Cosmology","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.388864Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:196028458296af87227c2de6e1b5d6de5a40742bcf170aa02c5dd160b1b677f1","observation_id":"0d2ccdc6-e8f3-4174-bfc5-d9ae933784f8","resolution":{"observed_at":"2026-08-07T05:05:41.388864Z","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":"2026-08-07T05:05:41.393050Z","title":"High-redshift cosmology by Gamma-Ray Bursts: An overview","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.393050Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:0c6f0835d078b4360dccf7d0729d23f81dc9c59cae42979495315f2430003e61","observation_id":"313ebfa5-4fb2-46ad-bd84-9ac0bf693e11","resolution":{"observed_at":"2026-08-07T05:05:41.393050Z","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":"10.3390/galaxies13020015","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"The Observed Luminosity Correlations of Gamma-Ray Bursts and Their Applications","venue":"Galaxies","work_id":"1436354d-625b-4b06-8edc-c2b2b6b9cba6","year":2025},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.396965Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:c87c2c7a7b230ea7df49f0f73c4183678d44b3ce8c470264aa8a4f4ee5c18edf","observation_id":"579da6f9-d94d-4d53-bf1f-2554b3c20e8b","resolution":{"observed_at":"2026-08-07T05:05:42.701663Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.401306Z","title":"Cosmology-Independent Distance Moduli of 42 Gamma-Ray Bursts between Redshift of 1.44 and 6.60","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.401306Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:2e64123459a28a2b9e0dc7ff50af31011d977afeac3f5b969850e7dc5df0962a","observation_id":"0ba54445-28a3-4ac4-9d88-3e3b36f30a84","resolution":{"observed_at":"2026-08-07T05:05:41.401306Z","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":"2026-08-07T05:05:41.410433Z","title":"An updated gamma-ray bursts Hubble diagram.Mon","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.410433Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:e38ea51785443cbd37a13729b01a9a9587171179ae6ee6cedc01b347907623a0","observation_id":"235ff05e-a3a0-4a5f-9de9-8846b8f8e1c7","resolution":{"observed_at":"2026-08-07T05:05:41.410433Z","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":"10.1051/0004-6361/201014522","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"A cosmographic calibration of the Ep,i - Eiso (Amati) relation for GRBs","venue":"Astronomy and Astrophysics","work_id":"51def1cb-8348-48f2-bbab-9095f70a2ba6","year":2010},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.418928Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:09fd999380f9ba130c4bd99a2e2f4aa7ba1d4bc73461c63b7db2a61458368802","observation_id":"dec6cde9-9cab-4ddb-80c9-137864148af7","resolution":{"observed_at":"2026-08-07T05:05:42.679389Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1142/s0218271812500162","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Calibration of GRB Luminosity Relations with Cosmography","venue":"International Journal of Modern Physics D","work_id":"9b7135ae-ab12-4b72-b47f-924668b18522","year":2012},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.423476Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:f4522902aa3768ce419d9ae06684e92b725bd7825af1a1088c771c13eec302d2","observation_id":"1505a76c-218e-445d-a45f-4062fe9bc55a","resolution":{"observed_at":"2026-08-07T05:05:42.665857Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.427776Z","title":"Cosmological models and gamma-ray bursts calibrated by using Padé method","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.427776Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:7deb2074c6f205a216a7d45078e0efe51066583682ef048ae42997ff9cfb1510","observation_id":"e0316796-810d-4246-a2ea-197188dbc246","resolution":{"observed_at":"2026-08-07T05:05:41.427776Z","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":"2026-08-07T05:05:41.431402Z","title":"New measurements of Ωm from gamma-ray bursts","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.431402Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:5b2f9b5f5f5bc17a5841343a127294eccd3c11c12c554fceed1cc0b17aef1720","observation_id":"06828237-5c70-41d5-8963-ad3311c609f0","resolution":{"observed_at":"2026-08-07T05:05:41.431402Z","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":"2026-08-07T05:05:41.435087Z","title":"B.; Zaninoni, E","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.435087Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:ecb24846df46913e480a6a26eb6501e06b556bb238df3e10aba2155eaa31dbf1","observation_id":"8225993e-5b2e-428f-8ff2-6e667cb4b6ab","resolution":{"observed_at":"2026-08-07T05:05:41.435087Z","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":"2026-08-07T05:05:41.438730Z","title":"Reconstruction of dark energy and expansion dynamics using Gaussian processes","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.438730Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:2d018cf4ab9781d4a8326d67ec18825ea8bac2a2f01ef16004c81d695d424ff5","observation_id":"22466989-c275-4ac0-968b-2cb3b3085d80","resolution":{"observed_at":"2026-08-07T05:05:41.438730Z","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":"2026-08-07T05:05:41.442327Z","title":"Constraints on the Cosmological Parameters with Three-Parameter Correlation of Gamma-Ray Bursts","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.442327Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:052a854334c63e77e3278c6440879a63a8ccb64c68950a70c0b43118c37562cd","observation_id":"17f223a0-9ff3-4a99-a7f0-70ed50ab6306","resolution":{"observed_at":"2026-08-07T05:05:41.442327Z","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":"2026-08-07T05:05:41.445893Z","title":"Calibrating Gamma-Ray Bursts by Using a Gaussian Process with Type Ia Supernovae.Astrophys","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.445893Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:b54deca106ac1c6365987cdc8803630520afdac89b8db45a74cd05c8b9481e84","observation_id":"b9ee0bbe-142c-4ec8-be46-d6aa9e9c93af","resolution":{"observed_at":"2026-08-07T05:05:41.445893Z","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":"10.1088/1475-7516/2023/09/041","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Cosmography via Gaussian process with gamma ray bursts","venue":"Journal of Cosmology and Astroparticle Physics","work_id":"b339e7b6-8f38-4d4b-98fa-9a43d171ec3d","year":2023},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.449516Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:80b40cd35d7c77cb4c9ba7c7c250a0567ce82ff8439ba89ed0295d08660d52f7","observation_id":"1e65e9d4-db60-4061-9714-6478b0ae13fb","resolution":{"observed_at":"2026-08-07T05:05:42.608111Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/1674-4527/ad8a07","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Testing the Phenomenological Interacting Dark Energy Model with Gamma-Ray Bursts and Pantheon+ type Ia Supernovae","venue":"Research in Astronomy and Astrophysics","work_id":"69c47cb2-b80d-4101-8c88-d83632b8e225","year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.453665Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:9abb04e72a597a5ec67a626ed79696d09881cd34a68baef3ad09093b71d7c77f","observation_id":"56739576-3d24-46f1-8a5a-7fc1720e6b61","resolution":{"observed_at":"2026-08-07T05:05:42.594916Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10509-024-04340-4","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:03:40.895421Z","title":"Constraining the emergent dark energy models with observational data at intermediate redshift","venue":"Astrophysics and Space Science","work_id":"46b974e3-70ae-4fec-95c6-6ac8c4659b3d","year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.457624Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:c86f3ba82938c825c7492b2d6d06887ab23d14828a57c1e552d8dff4088a94b2","observation_id":"4afe8689-31cf-4bac-a800-d76652d1fd38","resolution":{"observed_at":"2026-08-07T05:05:42.581617Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.461800Z","title":"Constraints from Fermi observations of long gamma-ray bursts on cosmological parameters","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.461800Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:8919b5249a9e94987c25361a92d15e51e8586c741f21e51517e932d4b16aafdd","observation_id":"323fcd9a-d48c-4a3c-9978-d82a30217ab4","resolution":{"observed_at":"2026-08-07T05:05:41.461800Z","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":"2026-08-07T05:05:41.465491Z","title":"Constraints on cosmological models with gamma-ray bursts in cosmology-independent way","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.465491Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:71971e331e7b5dd2b186f7c59cfa9ab354f9739f73e5827133262bcb077e0a24","observation_id":"50420a70-534d-4efa-b5aa-98a0940cd8c0","resolution":{"observed_at":"2026-08-07T05:05:41.465491Z","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":"2026-08-07T05:05:41.469383Z","title":"Using H(z) data as a probe of the concordance model","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.469383Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:a72f343a096e2ea1636e0aa6784f9c591e290699a9a89a55bc3865dae0986e29","observation_id":"59a8a932-438f-459d-8726-1bd7351a755a","resolution":{"observed_at":"2026-08-07T05:05:41.469383Z","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":"2026-08-07T05:05:41.472886Z","title":"An Improved Method to Measure the Cosmic Curvature","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.472886Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:e350e5a7490bc1865bc89094397b66c618c1fdcd364b0517ce657bd1c7f9cd14","observation_id":"57ee25aa-d725-4e2b-8844-a6f81fd15883","resolution":{"observed_at":"2026-08-07T05:05:41.472886Z","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":"10.1088/1674-1137/43/3/035103","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Testing the fidelity of Gaussian processes for cosmography","venue":"Chinese Physics C","work_id":"75752a24-8406-454f-885a-e03e662ce181","year":2019},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.476753Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:973021d53b777dec17c4b904b534ee495403c8a5ee0d61687393d4a226934ba3","observation_id":"b179c8fb-d23e-4dc7-8531-19583eeb1409","resolution":{"observed_at":"2026-08-07T05:05:42.533027Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.480608Z","title":"Model-independent calibrations of gamma-ray bursts using machine learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.480608Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:8bf4016661e1a12ef125ba3d733b06327a1925bd10b34e3bec85e4c05eeffb3f","observation_id":"7ee7a811-8677-4eb2-af06-b1cc527fb910","resolution":{"observed_at":"2026-08-07T05:05:41.480608Z","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":"10.1140/epjc/s10052-023-11734-1","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Measuring the Hubble constant with cosmic chronometers: A machine learning approach","venue":"The European Physical Journal C","work_id":"fa6338cf-0438-4019-be7b-69a9f80457e4","year":2023},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.484936Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:2a5947e1d86bb7b46eee0c7655a8a4df23b0d982c2625df11a82066ac1133b8f","observation_id":"bdf79d94-4a47-413c-8cc8-1b1a706603bb","resolution":{"observed_at":"2026-08-07T05:05:42.510747Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/s10509-025-04401-2","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T00:03:40.895421Z","title":"Model-independent gamma-ray bursts constraints on cosmological models using machine learning","venue":"Astrophysics and Space Science","work_id":"bbe4dc3d-8b07-4974-9b73-3d071318c9be","year":2025},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.488996Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:19fb0ed73c1293528c291642a17804a5fd47c0dd331d873ed604470108914d9e","observation_id":"440ff213-d20c-46ed-86f4-4a59b598890e","resolution":{"observed_at":"2026-08-07T05:05:42.497976Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.492908Z","title":"The Pantheon+ Analysis: The Full Data Set and Light-curve Release","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.492908Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:0636e4e815ebf801f08cc5ea0d421754f756e1e720d0f7c9e84721e8afe81ac9","observation_id":"cfb87391-9837-4e67-bb40-f4568194052b","resolution":{"observed_at":"2026-08-07T05:05:41.492908Z","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":"10.48550/arxiv.2410.08369","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Estimating Cosmological Parameters and Reconstructing Hubble Constant with Artificial Neural Networks: A Test with covariance matrix and mock H(z)","venue":"arXiv (Cornell University)","work_id":"2a17cad9-ae72-4f99-8a6d-877042822699","year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.496642Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:d3eacacf54dadde13256cf5da83521e4d853e64be67f42db45c561b17fd12d9a","observation_id":"2e2d2d4e-0913-4a58-8303-c58067d7d448","resolution":{"observed_at":"2026-08-07T05:05:42.474609Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/1475-7516/2022/10/069","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":null,"venue":"Journal of Cosmology and Astroparticle Physics","work_id":"23123c25-0671-4982-ac7a-5be145f9c30b","year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.320613Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:3489951fd6f47c00de7b7df775e669805dfe2ccb6527618c7c48e37a68245044","observation_id":"bd7d8670-7cda-4b76-a144-a56d914b7133","resolution":{"observed_at":"2026-08-07T05:05:42.914063Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19953","last_updated":"2025-05-30T15:38:15Z","snapshot_observed_at":"2026-08-12T23:54:17.915582Z","submitted_at":"2024-05-30T11:18:17Z","title":"Do high redshift QSOs and GRBs corroborate JWST?","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.19953","snapshot_observed_at":"2026-08-07T05:05:41.504730Z","title":"The CosmoVerse White Paper: Addressing observational tensions in cosmology with systematics and fundamental physics","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.504730Z"},"links":{"cited_paper":"/paper/2405.19953","citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:a56985aaa274a379c19b079b79808bfead7139a98afeefebf061df1257bbe584","observation_id":"efcdae4d-dd8a-4975-904a-d4283386b9e8","resolution":{"observed_at":"2026-08-07T05:05:41.504730Z","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":"10.1088/1475-7516/2020/03/008","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"A deep learning approach to cosmological dark energy models","venue":"Journal of Cosmology and Astroparticle Physics","work_id":"484eea3d-9065-4262-b5dc-69ea282ef4b6","year":2020},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.509020Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:b58c4abaa7039228c137b219c6394544ffbfc9cfddf2689b8fbbbd3c3fb7a475","observation_id":"3ba5aa91-bf53-4647-9b4e-2436424e65b9","resolution":{"observed_at":"2026-08-07T05:05:42.388225Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.500850Z","title":"Neural network reconstruction of late-time cosmology and null tests","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.500850Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:0005a3fd5dd5cc06e8cc900efe4874f2fa80e9e96247e1a108a5d77d53eacc3b","observation_id":"40b32f7e-d8e1-4487-8831-8fd019fa69d4","resolution":{"observed_at":"2026-08-07T05:05:41.500850Z","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":"10.48550/arxiv.2502.11443","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Constraining the Hubble Constant with a Simulated Full Covariance Matrix Using Neural Networks","venue":"arXiv (Cornell University)","work_id":"5905d99c-7f97-47e9-9b6b-15cee9f8611b","year":2025},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.517261Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:c3c047409e878a2860fb16c70f8c972b7b8f54a0925bf822e775575fa29e80c4","observation_id":"d3ec992b-36a4-49a2-831e-9217910c41e9","resolution":{"observed_at":"2026-08-07T05:05:42.360109Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.521461Z","title":"Reconstructing Functions and Estimating Parameters with Artificial Neural Networks: A Test with a Hubble Parameter and SNe Ia","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.521461Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:c7f48f25297f2d31d541fb8678c5a1d33d551848814761112a31866b7272508b","observation_id":"23afd6b4-f892-49ff-ab8e-8b1b51bfe0f3","resolution":{"observed_at":"2026-08-07T05:05:41.521461Z","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":"10.1140/epjc/s10052-023-11435-9","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Neural network reconstructions for the Hubble parameter, growth rate and distance modulus","venue":"The European Physical Journal C","work_id":"1ce703ab-68b7-49e2-9d2b-9070760f96af","year":2023},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.512842Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:2bcd96f8ea30aaba507e36a19fd0f3d81a143da2aa437d9e0188f7181c395b39","observation_id":"61cdc28b-9049-4b7c-bd79-715d217df9ca","resolution":{"observed_at":"2026-08-07T05:05:42.374556Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3847/1538-4365/ad5558","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring Its Applications","venue":"The Astrophysical Journal Supplement Series","work_id":"e1250654-fa08-4c4c-a76c-e932c46cc539","year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.528990Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:c2e7ef5394d75c6b562f077a3af18aaf6a70ebbae2e8c8050006dff9e28a3a44","observation_id":"03d7f2eb-90e5-469d-b20f-234589735f85","resolution":{"observed_at":"2026-08-07T05:05:42.262477Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.03773","last_updated":"2026-05-07T16:04:58Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-11-06T09:10:25Z","title":"Model-independent calibration of Gamma-Ray Bursts with neural networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.03773","snapshot_observed_at":"2026-08-07T05:05:41.532842Z","title":"Model-independent calibration of Gamma-Ray Bursts with neural networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.532842Z"},"links":{"cited_paper":"/paper/2411.03773","citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:d8bca05264060ceb9434b96a0f12219e66cddfbaaf911bd61e3b6c94cea1ec3a","observation_id":"d9bd4eeb-1215-41e8-b1f8-53af3d1fa218","resolution":{"observed_at":"2026-08-07T05:05:41.532842Z","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":"2026-08-07T05:05:41.525277Z","title":"A Nonparametric Reconstruction of the Hubble Parameter H(z) Based on Radial Basis Function Neural Networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.525277Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:6bd5fcef552afe5931af6b027c34f823d505eabd6ca34c5adb34fc1e7dc71c69","observation_id":"0fcaede6-349e-4518-9256-a7d840128e93","resolution":{"observed_at":"2026-08-07T05:05:41.525277Z","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":"2026-08-07T05:05:41.540525Z","title":"A Fundamental Plane for Long Gamma-Ray Bursts with X-Ray Plateaus","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.540525Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:bef84eff29b0ff332deda200f8d85d1fdf85e4aef175b6916c1a823679d1f505","observation_id":"d89b473b-0338-45af-9ca0-b1d59f52de2c","resolution":{"observed_at":"2026-08-07T05:05:41.540525Z","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":"2026-08-07T05:05:41.544319Z","title":"Gamma-ray bursts calibrated from the observational H(z) data in artificial neural network framework","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.544319Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:4a446223c9c3dde2ef2ec4839752bdee35713eaad496cdb902fe58393f552dc2","observation_id":"d089274a-d03e-44a6-a41b-6db35c234115","resolution":{"observed_at":"2026-08-07T05:05:41.544319Z","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":"2026-08-07T05:05:41.537009Z","title":"A time-luminosity correlation forγ-ray bursts in the X-rays","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.537009Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:702da169efc7263d969cfd38c0b53380c35dd3bddeafd959905bf3e40dd34ee3","observation_id":"26dc0b53-6314-4cfd-bb3b-e50070d30483","resolution":{"observed_at":"2026-08-07T05:05:41.537009Z","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":"2026-08-07T05:05:41.552223Z","title":"E iso-Ep correlation of gamma-ray bursts: Calibration and cosmological applications","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.552223Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:0e31ce7795f83dee0bf3ea3a5c96ce24a59fec811a3ff9a9b6723309d473d533","observation_id":"08257dce-28d5-4263-b320-56831aac01f9","resolution":{"observed_at":"2026-08-07T05:05:41.552223Z","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":"10.1088/1475-7516/2022/04/016","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Neural networks and standard cosmography with newly calibrated high redshift GRB observations","venue":"Journal of Cosmology and Astroparticle Physics","work_id":"f5645ccf-0733-4f61-83b8-2dd7ecc416b3","year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.556128Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:8ac9ddb42665402aabd67d8eac26bc4c20dce5d7bf90e184fa9dddcf315b8a08","observation_id":"a68edf46-dba6-46c8-af83-3340950e96c6","resolution":{"observed_at":"2026-08-07T05:05:42.203210Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/1475-7516/2024/10/093","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Testing the standardizability of, and deriving cosmological constraints from, a new Amati-correlated gamma-ray burst data compilation","venue":"Journal of Cosmology and Astroparticle Physics","work_id":"84868413-1603-41d1-9abb-e51c9789652e","year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.548251Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:7185d342d625b2e5858f45eb9359425a7cd910ca6b05a6f2bf6c70af6671e36e","observation_id":"ff03f966-9c2b-4347-96ea-33dc095cdf2e","resolution":{"observed_at":"2026-08-07T05:05:42.225988Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/mnras/stab2932","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Reconstructing the Hubble diagram of gamma-ray bursts using deep learning","venue":"Monthly Notices of the Royal Astronomical Society","work_id":"be5868e0-f51d-495b-b930-a4eae782bad9","year":2022},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.564049Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:c95cff6d47b94b38fe4e3aa3931e2bc7873eddcd5f28158aaaca637a6f5fbea4","observation_id":"3e7d16ec-5e81-4119-9443-c428fdda228f","resolution":{"observed_at":"2026-08-07T05:05:42.175080Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.568015Z","title":"Learning representations by back-propagating errors","venue":null,"work_id":null,"year":1986},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.568015Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:9128c46933e7bec975d208be799882ecc1b44fe17a53dc18d407bd8496c8471a","observation_id":"a31be0ce-9823-4b48-a80b-f0c8d982541f","resolution":{"observed_at":"2026-08-07T05:05:41.568015Z","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":"10.3847/1538-4357/abcd92","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Model-independently Calibrating the Luminosity Correlations of Gamma-Ray Bursts Using Deep Learning","venue":"The Astrophysical Journal","work_id":"e13535e0-3b75-4b01-bd62-1d8c00a6bb20","year":2021},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.560131Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:00602a4c1a66257602560e63c11c1692ba0ba8a6318e3201baaae184caa9de90","observation_id":"3bca329b-6d35-4a9f-8400-439bcd47587f","resolution":{"observed_at":"2026-08-07T05:05:42.188772Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1506.02157","last_updated":"2016-05-25T18:46:56Z","snapshot_observed_at":"2026-07-06T04:20:07.263346Z","submitted_at":"2015-06-06T14:42:06Z","title":"Dropout as a Bayesian Approximation: Appendix","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.02157","snapshot_observed_at":"2026-08-07T05:05:41.576156Z","title":"Dropout as a Bayesian Approximation: Appendix","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.576156Z"},"links":{"cited_paper":"/paper/1506.02157","citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:76d0af5951fd5f5d17d18a9c977bf897697ff477a5a17280a63fe4a9a86a9fc0","observation_id":"ea92b51a-bab1-4eb4-8e38-4290a27bc94c","resolution":{"observed_at":"2026-08-07T05:05:41.576156Z","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":"2026-08-07T05:05:41.580878Z","title":"Dropout: A Simple Way to Prevent Neural Networks from Overftting","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.580878Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:427190edc5eea617d60ed2c3a461912620a034018d0cc0f98a01c2c50061cf3e","observation_id":"c7dce690-e93c-445f-82a6-c8f13e431a15","resolution":{"observed_at":"2026-08-07T05:05:41.580878Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1506.02142","last_updated":"2016-10-04T16:50:26Z","snapshot_observed_at":"2026-08-14T05:08:06.217969Z","submitted_at":"2015-06-06T12:30:43Z","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1506.02142","snapshot_observed_at":"2026-08-07T05:05:41.571994Z","title":"Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.571994Z"},"links":{"cited_paper":"/paper/1506.02142","citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:f6c80923c4943c68d024f0ab8dd10a2b77af9429d42556380515a309bab5106e","observation_id":"8066fc61-4bd9-4b95-a53f-cbf3ed6c3802","resolution":{"observed_at":"2026-08-07T05:05:41.571994Z","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":"2026-08-07T05:05:41.589384Z","title":"emcee: The MCMC Hammer","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.589384Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:5c590b25951027f2dfe8ba8ad4b6f4830b3424dc6e38f55f57470a47919de08e","observation_id":"48aedb92-c27a-46dc-a091-789d3610b154","resolution":{"observed_at":"2026-08-07T05:05:41.589384Z","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":"10.1086/320630","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Dust Extinction Curves and Lyα Forest Flux Deficits for Use in Modeling Gamma-Ray Burst Afterglows and All Other Extragalactic Point Sources","venue":"The Astrophysical Journal","work_id":"ebb4d3ad-8fa7-4528-9941-0af99a7e8d85","year":2001},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.593019Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:97d05bacd3725cdd2735c38aa7b2293a6b8865ea0bbf6e1e854d389a7fc15f04","observation_id":"543af2a0-6dcd-4d40-a37b-60bfb1756d79","resolution":{"observed_at":"2026-08-07T05:05:42.100066Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/mnras/stae1120","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Constraints on cosmological models from quasars calibrated with type Ia supernova by a Gaussian process","venue":"Monthly Notices of the Royal Astronomical Society","work_id":"97d23592-b7ad-4ec5-8b69-62bbee2e2bd0","year":2024},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.585597Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:b24fe147e9d37b592fb33fc3465e376e250cd4799127efae872b34f175fe74b2","observation_id":"9be3f240-9b94-4bd5-bf5e-1b13534fb794","resolution":{"observed_at":"2026-08-07T05:05:42.122827Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.600574Z","title":"Testing dark energy models with gamma-ray bursts calibrated from the observational H(z) data through a Gaussian process","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.600574Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:7c8f9c3a416fc3cf8174573dbb8b5495d954a0d7f31404a427f74833b3209742","observation_id":"5bc620e4-2696-4430-a416-0b7e6dfbcede","resolution":{"observed_at":"2026-08-07T05:05:41.600574Z","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":"10.1093/mnras/stab1669","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Prospects of high redshift constraints on dark energy models with the Ep,i-Eiso correlation in long gamma ray bursts","venue":"arXiv (Cornell University)","work_id":"a27b147e-40dc-4928-8be7-647609906294","year":2021},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.604659Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:455873c46e1bc5e6925edfc6012f416a17a0fdf89d9f5d175750e556c05abaae","observation_id":"d0e42729-09b9-4d1a-9748-773b9d4064dc","resolution":{"observed_at":"2026-08-07T05:05:42.067453Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.596882Z","title":"Measuring Cosmological Parameters with Gamma Ray Bursts","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.596882Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:96d3d8e18b6e7e487584ba0bfa1d43aac17e3ca68307336708f31af858cb8bd8","observation_id":"8da5befb-5a94-4866-86ba-f4382f1a647f","resolution":{"observed_at":"2026-08-07T05:05:41.596882Z","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":"10.1093/mnras/stv2471","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Model-independent distance calibration of high-redshift gamma-ray bursts and constrain on the ΛCDM model","venue":"Monthly Notices of the Royal Astronomical Society","work_id":"87453343-fba2-4af1-ac2e-185b041171da","year":2016},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.612493Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:32e04c3504d9aea63e4495f1d5920051692ba95fbd43d3665521ae088210e556","observation_id":"f58d09e1-98d5-4fac-ae5b-8e7bb88d0e7b","resolution":{"observed_at":"2026-08-07T05:05:42.044143Z","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":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3847/1538-4357/aa8725","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Model-independent Constraints on Cosmic Curvature and Opacity.Astrophys","venue":"The Astrophysical Journal","work_id":"9c808285-6c9c-40c6-9403-c4444553101e","year":2017},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.616477Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:aaa33d401b6fd36125d4f748a5de76af38fcc2a3931c238d1dc36e26661621ee","observation_id":"950c0c4f-4a42-4a24-9632-d7901c5a17ee","resolution":{"observed_at":"2026-08-07T05:05:42.030603Z","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":"2026-08-14T06:32:18.44784+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":"2026-08-07T05:05:41.608444Z","title":"Gamma rays bursts: A viable cosmological probe? J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.608444Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:d86627ac895bbf0853b9219fb748ccdbf36811f1eb09557996f2ecb952ecc987","observation_id":"96c8c9b2-bd60-48c9-bec7-105488651664","resolution":{"observed_at":"2026-08-07T05:05:41.608444Z","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":"2026-08-07T05:05:41.624634Z","title":"Exploring the Expansion History of the Universe","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.624634Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:54a388ca9661fb92d8fafa7f3880fad7f00a6caf328e57ef2413157d3a6ae52e","observation_id":"38155c3e-b792-4864-88c8-167abb926ad7","resolution":{"observed_at":"2026-08-07T05:05:41.624634Z","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":"2026-08-07T05:05:41.628144Z","title":"Improved constraints on the expansion rate of the Universe up to z ~1.1 from the spectroscopic evolution of cosmic chronometers","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.628144Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:e6e48725b2de676289d33d15e1cab34b97541601885fb5c039af06f53b367334","observation_id":"412bb255-802c-413b-a86d-7371c4b1655a","resolution":{"observed_at":"2026-08-07T05:05:41.628144Z","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":"2026-08-07T05:05:41.620746Z","title":"Accelerating Universes with Scaling Dark Matter","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample","version":2},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-08-07T05:05:41.620746Z"},"links":{"citing_paper":"/paper/2506.08929"},"observation_digest":"sha256:e5e85ac5d725fc120e01003b2189fa17a836ea93cd7ee2cc510313562b149f1d","observation_id":"14d9de9b-1668-4fcd-bc53-1ac127687a63","resolution":{"observed_at":"2026-08-07T05:05:41.620746Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2506.08929","last_updated":"2025-07-24T10:22:21Z","latest_version":2,"primary_category":"astro-ph.CO","snapshot_observed_at":"2026-08-11T16:08:34.420415Z","submitted_at":"2025-06-10T15:52:42Z","title":"Gamma-Ray Bursts Calibrated by Using Artificial Neural Networks from the Pantheon+ Sample"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":58,"verified_exact":40,"verified_fuzzy":0},"total_outbound_references":114},"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 100 of 114 outbound references and 0 inbound Pith citation observations for arXiv:2506.08929."}