{"as_of":"2026-08-08T07:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:34d671aea9f5e86251d6e374b920d632cd663d0a46775933748a9e6a357f571f","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":"2026-08-08T06:32:00.761636+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":"2026-08-07T04:56:34.919092Z","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":"sha256:9259bef1c39978fba7afae5df12c33d02b1f81ef4c1fca360343fc9c2a4f1c0f","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":"2026-08-07T04:56:34.919092Z","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":"sha256:94e811bd42c85c8c8d77ceedf43342aa44123dab8d355561a818c6b69b541f1a","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":"2026-08-07T04:56:34.919092Z","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":"sha256:96d7f50e38a327fef6bfc2044f8fa760801163586c7c9bd2d61e0e815f8c5146","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":"2026-08-07T04:56:34.919092Z","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:f320420276de3f48ab09e72ccb317a76cb36d7bbb295c925fd0d0112f0c4561a","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":"2026-08-07T04:56:34.919092Z","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":"sha256:8c0b2c8fa650218ffb71859ff2a123a05f4c45e1e0e98154c5ac48bafcc3cbf6","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:09:17.335121Z","title":"Gamma-Ray Burst Hubble Diagram to z = 4.5","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:4c2acb7299e19f29b4082cb272830b63c8ef04802bd843e5f8b30a0652877dd6","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:75c996c4f8979509c45bcb42386ef957bd1f57a9ae4bec8ba1397acd04e849ae","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":"2026-08-07T04:56:34.919092Z","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":"sha256:59c8775348a5a36db640fe24808fe131688895b0a673b269ef66329da14f507e","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":"2026-08-07T04:56:34.919092Z","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:a654d7948ba7033b29c7e7b3088f541cae03c227d39d1dd74fd1bf2416006837","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:09:16.702656Z","title":"A Cosmology-Independent Calibration of Gamma-Ray Burst Luminosity Relations and the Hubble Diagram","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:d81aad7821da43ad9f3860fd911827a513ce272eff7f1ecbd6f8c84f59918e6f","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:87c3ead56f23ca49abf3c930822a116f247e94dfe233f3f782dc97332eed5456","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":"2026-08-07T04:56:34.919092Z","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":"sha256:6fe062119de146448f2f496ddf3b2702d907bf894de85b3831262cfe891826ba","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:09:16.367324Z","title":"Cosmography by GRBs: Gamma Ray Bursts as possible distance indicators","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:c3ea7fc5026795713de90a2c6e3ccf79089b1b7a95114af9e5d84be16eb91454","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:7abe74e1863f3aee7b65f2d01fe0839483250cbd69278e0ecd7014587e12adfd","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":"2026-08-07T04:56:34.919092Z","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":"sha256:9ace5b287898241c2886f11a65ae471fa9b2f2ef2d4011c152ea69e236c0139b","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:09:16.080258Z","title":"Constraints on cosmological models and reconstructing the acceleration history of the Universe with gamma-ray burst distance indicators","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:46f185df33ee35f72903ab7d716294302afc5d7faf630fd51bc8fbbe72fa1052","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1051/0004-6361/201015919","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:09:15.768134Z","title":"Constraints on the generalized Chaplygin gas model including gamma-ray bursts via a Markov Chain Monte Carlo approach","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:cc0825517b25ca2523c4ef3a711019c409510db219d35b2c4f8fe90cf217e58e","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:09:15.581448Z","title":"Reconstructing the cosmic expansion history up to redshift z = 6.29 with the calibrated gamma-ray bursts","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:cfcc4b5498841d2711e6638f547d0c9db1172a1efdb3af152b2c23d72e59a349","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:09:15.355998Z","title":"Observational constraints on cosmological models with the updated long gamma-ray bursts","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:351fc105c2b34077901e91ed03742c53fefbbf5631dc4efbdcabe9ded500b49f","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/mnras/stac2925","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:09:15.211028Z","title":"Intermediate redshift calibration of gamma-ray bursts and cosmic constraints in non-flat cosmology","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:4ca5b9913650bbeb8918ec014d18a961fbd2f2d855f91119083f58ab1e406fb7","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:0b1464d9662ddd132f0fa988f69385ad95ec3f49e59dd9ad8346418c5e894c4f","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":"2026-08-07T04:56:34.919092Z","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":"sha256:1ae542cfde4cfdf6707c092364ec5ca11c3c1fdbeca9b7f00bf1acb7aa2e610d","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.950832Z","title":"Calibration of Gamma-Ray Burst Luminosity Correlations Using Gravitational Waves as Standard Sirens","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:670a8a77fc7b5a1f13c4922b59e175897b3425d213cb1044a66f2ae2f39c462c","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1051/0004-6361/202140895","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.937278Z","title":"Redshift evolution of the Amati relation: Calibrated results from the Hubble diagram of quasars at high redshifts","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:ad84a7e4e7e38327ad13d72300a59d40ff8dc1d7633fc3e78180a0b25355f9f4","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3390/galaxies12060069","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.923576Z","title":"Calibration of Luminosity Correlations of Gamma-Ray Bursts Using Quasars","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:4ee6957ebd9cb09c3caa8695198e162be503553132bdfcbdbe859fe0474b963d","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:6064c15dc8193a96cd92453fa5ee2df5743e50ef6fad4a59a85bbbaea6e58966","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":"2026-08-07T04:56:34.919092Z","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":"sha256:823db9a14f018991ff9976dfe9f17567238ab8314fea55b3c412b2ea077723b7","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":"2026-08-07T04:56:34.919092Z","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:de9c1bca65407a0d07af5d00bf9d9f234271cce3960bf69c771ac9b58ccc502f","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":"2026-08-07T04:56:34.919092Z","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:c2f1c9c99c861605e06d7d1e9e19dd12eae8c38f4e4e988f63acaab72c78dbfa","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.878845Z","title":"Constraints on cosmological parameters from gamma-ray burst peak photon energy and bolometric fluence measurements and other data","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:6267eaf0fb16268cfcd035a49a00f284d211437f056b7c7d29d6f3c11ad43add","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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:cd0002be9e6ca5bf2469ba2d14c3365994fdc05e84e1485d5fa86fddbb1eff3b","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.856653Z","title":"Using lower redshift, non-CMB, data to constrain the Hubble constant and other cosmological parameters","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:d9385d24dea1ef37890afbf0091f8d7d266fbb060a3a64116e33d4cd931a2ec6","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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:ba48776073f8f73a07388f4ff2a818f636d1f8f0afcbfe6237a950ac8771e34b","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":"2026-08-07T04:56:34.919092Z","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":"sha256:71686d187ca909043612c37d8a89b1c4a051e52c80769649ae044084d8095a04","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.834496Z","title":"Detection of gamma-ray burst Amati relation based on Hubble data set and Pantheon+ samples","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:23cb504b9c328604c3a25df82121597888cf65543fe43cbb75a8bd426bc2f14f","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:40c2bffe46552886b6f2b99426df7c798c287b7f44f9ba2645a4a517055c8e2f","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":"2026-08-07T04:56:34.919092Z","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":"sha256:893e981a13e713ec32af52a4001245129725f65607e6e8366e8fa85022e4e450","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.802932Z","title":"The Improved Amati Correlations from Gaussian Copula.Astrophys","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:f9ca08acc8868e0acaf3ebaa976ed21485126b74dc52c33f0d87f656a643154c","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:68005f1d3bf95bd73d80877c4897c146a18df7ba8252ece0dc4b0bfd53611b21","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.780814Z","title":"Testing Non-Coincident f (Q)-gravity with DESI DR2 BAO and GRBs","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:866b2943adb7a7b9a747488bc9e27980a36d3bc77e35e91d503b6e7d5f384b70","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:6167c87c6be9b0512bb4c07905edd4e710ea2b7dab580803fe6d7fabe1c522e4","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":"2026-08-07T04:56:34.919092Z","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:a7dbb007a10a0ca1ca1bd64efb3994bfe82b796f8765af75ecc0ff15bff74f99","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.697409Z","title":"The Observed Luminosity Correlations of Gamma-Ray Bursts and Their Applications","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:385e94d1470f9c80a84270eb05937aee1848bcc5168de35cb68c6945132f49ca","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:0d9cb87b7c74eda879b07cc353980a37acff7577bbc26f9486c863213a0bd93a","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":"2026-08-07T04:56:34.919092Z","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":"sha256:13baa1cac1e5c52330612b79f17a97e6721f75921e904ac724f07292e202ed2b","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.675100Z","title":"A cosmographic calibration of the Ep,i - Eiso (Amati) relation for GRBs","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:694d829f9de35404e78b856271c9ed9b36a63652d933631568abb4a7be5f407e","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1142/s0218271812500162","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.661416Z","title":"Calibration of GRB Luminosity Relations with Cosmography","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:bcac7e7faf2291bb9f8ab7b154b34caca93d485b4f34b8d5b86eb42e6fe1d017","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:3080d7fed18a4d11702345ef6647c175608216bf059926589a464397ba513199","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":"2026-08-07T04:56:34.919092Z","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":"sha256:34c9801f085b261a9b21c617dd7b4f69fa45b76f235e5bfe87c4d3c7439a899b","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":"2026-08-07T04:56:34.919092Z","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":"sha256:034fe28d48864d46afc8b9bc0f63422c1905179925a6eb519c3c985f4ff83289","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":"2026-08-07T04:56:34.919092Z","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":"sha256:7ec1928356a00680520bd5da05331fdd2a699142962516855f3743df67a7b1ed","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":"2026-08-07T04:56:34.919092Z","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":"sha256:8d7ac93a646d6a777ce24d72a24334d4df61297a5b2f8b484806301d468e85bd","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":"2026-08-07T04:56:34.919092Z","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:c23eba548069abd5cdd5f1258384f56c09a190455b2a9612f2a8ac1ea209abfa","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.604046Z","title":"Cosmography via Gaussian process with gamma ray bursts","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:172a91f01c3d49690296e39662f3869fbce502b37f0508887e85bb2cdcc5a9f8","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/1674-4527/ad8a07","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.590830Z","title":"Testing the Phenomenological Interacting Dark Energy Model with Gamma-Ray Bursts and Pantheon+ type Ia Supernovae","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:16db21131a2053435843ec2405a53ddff258d0167777c1adbffc1c28e6cbb0c6","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.577301Z","title":"Constraining the emergent dark energy models with observational data at intermediate redshift","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:9f50c18edb290f50408911bbd33ade977b734557b120056563bcf2a75dd534a3","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:0060e2e9da914e80b9cca0ff7e96cd08134e74114f1b72b8d784420c86e03ec2","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":"2026-08-07T04:56:34.919092Z","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":"sha256:5c6d9353332deb0aef5da04edf5d6bcdb94d043e2a1f9517a3c7a31950b17312","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":"2026-08-07T04:56:34.919092Z","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:c12c405f50587569f2c344d53fbdeb8e89f16f773dec499441f35538c880ea4f","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":"2026-08-07T04:56:34.919092Z","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":"sha256:1191204ea541a43d65ae583f3c6c31e345782d35dfa579d4d4ef45a088341c48","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.528908Z","title":"Testing the fidelity of Gaussian processes for cosmography","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:17346a353f12364c3f64f2fde0fb46a7269a3d1401192010e22003cc08faf857","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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:c2ed398a50ac25f7d6527cb33eee88b7dad595bfef19a3212285404dbfa13a3a","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.506772Z","title":"Measuring the Hubble constant with cosmic chronometers: A machine learning approach","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:5dcfec236852f5216776c40c2ae4b7e94bba35cbecffbd0a5631162298a4f3b7","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.493844Z","title":"Model-independent gamma-ray bursts constraints on cosmological models using machine learning","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:ec23a02b52cb998301707719517a4882c25d8a1005c96c4283ba6f9f01a9a9b8","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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:e4dbe230abc1d0d9c0a4d4fad39be6873f2716591d859cd141bd80ebdca359dd","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.470262Z","title":"Estimating Cosmological Parameters and Reconstructing Hubble Constant with Artificial Neural Networks: A Test with covariance matrix and mock H(z)","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:fa78ebafff29e040322a2c619b9a95a0d7795d1c21793f24c29cdf7318ac841d","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.910174Z","title":null,"venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:74d013034b853ce3a4de1a0ceb320242da81eb83ba5595f3b7bcbdd8274b6671","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.19953","last_updated":"2025-05-30T15:38:15Z","snapshot_observed_at":"2026-07-06T18:22:36.012485Z","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":"2026-08-07T04:56:34.919092Z","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:f293d5b4f3351c75b492306e33a0714d9f9bc4a632a682e72e8bdccb4a3d68cf","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.383532Z","title":"A deep learning approach to cosmological dark energy models","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:645fb9cae365aed3f8154ec533c3cf89bc25a55dc69eb898d01b544c61c0b10d","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:299b7ccfff2c13fc2e030c62b486873234b89e312aa8e1c8f10af8772e6991c8","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.356035Z","title":"Constraining the Hubble Constant with a Simulated Full Covariance Matrix Using Neural Networks","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:101e050729a8dd931318305d3faea9eb99161f23e2a7f0de6ac5845b647a3357","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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:e3acf51c4f37a7fa3596401bd425a97ea6458e05af2d4304da8f8752d532baf4","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.370378Z","title":"Neural network reconstructions for the Hubble parameter, growth rate and distance modulus","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:b19b139d8458e67be01b9d1fa82f2555a10aa756948701761288a0737fcef07f","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3847/1538-4365/ad5558","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.258186Z","title":"LADDER: Revisiting the Cosmic Distance Ladder with Deep Learning Approaches and Exploring Its Applications","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:2ca457d99806ead9e5cb14b93ec8181681bd2b449f5841000cc33e254997944e","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:0c10b992f933aeeda6dc4d01e0fd791e558b50e1f5f261e1e83bdeef32ba1392","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":"2026-08-07T04:56:34.919092Z","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:bdbfde999613bee9c88d29e0dc9af1be60ed23c66d910945f895b612d2b7be2a","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":"2026-08-07T04:56:34.919092Z","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:b4aabcdbbed62150eab5311fd587633de1cb716604d0c9219cf9fa2573ff1bca","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":"2026-08-07T04:56:34.919092Z","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":"sha256:6eb97652d71a32e28aabb6003927ed19a7567a09d3537f3478daab369fcc878c","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":"2026-08-07T04:56:34.919092Z","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":"sha256:5c023ccf9d8a6c8374e58267ed3e7e01cff1108cd59c17bdf4df3af4164b0864","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":"2026-08-07T04:56:34.919092Z","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:d70e1e814fbc992d15b654291bf11244429a143965ce2c55d80467bc0108e8dc","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.198790Z","title":"Neural networks and standard cosmography with newly calibrated high redshift GRB observations","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:3878c6d99cb14705f68787a4d1ff5070d2ba0212e903a191ca7cbd0567d070eb","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.221503Z","title":"Testing the standardizability of, and deriving cosmological constraints from, a new Amati-correlated gamma-ray burst data compilation","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:62b19b84be9217625972ba4d5231dc48f1a38ec2cd9ba12fea6460dd9534aebd","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/mnras/stab2932","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.170102Z","title":"Reconstructing the Hubble diagram of gamma-ray bursts using deep learning","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:2ef2751226a990d4ef8478f6b8d97c199612024ce833d2687427e5776a4af8f5","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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:a7302a5e606954f119be85c35879639659453391a7991160d7ad75af1f140eb2","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.184317Z","title":"Model-independently Calibrating the Luminosity Correlations of Gamma-Ray Bursts Using Deep Learning","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:bd26a6708506d99d9b53ade4aa0559799f765edfe10c9b96af8b0b34d39968bd","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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:e7861b82bec4c18a377d9732c92023055ab4d0548ddcc9145312db117e58fddd","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":"2026-08-07T04:56:34.919092Z","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":"sha256:8d74a080d7f3057da41e3c7cbb69de845a07436a89c0c47d94749a99cc1a91df","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":"2026-07-06T04:20:07.263346Z","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":"2026-08-07T04:56:34.919092Z","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":"sha256:1a4e09628038d816909a5114ca845dd1d2b8bb3e2b98a190549eeb06c7add8f3","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":"2026-08-07T04:56:34.919092Z","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":"sha256:38f895365be49cfcc0c106e8e68769b69ec5eb9c8c1472151743761fabeed4eb","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.095700Z","title":"Dust Extinction Curves and Lyα Forest Flux Deficits for Use in Modeling Gamma-Ray Burst Afterglows and All Other Extragalactic Point Sources","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:6b6ebdfb18db6646249c595460b1230fc9afd1a71be6858ba30988dfc258ec18","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/mnras/stae1120","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.118453Z","title":"Constraints on cosmological models from quasars calibrated with type Ia supernova by a Gaussian process","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:6385724759ffa9171eb8309701aa8bada7719655b5a301e1bf02e1b0aa200629","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:8c0a8e7bd45e78e8714e41c97443e19beae65a3093a0a9cd0b58e331e5a7a0ca","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.063082Z","title":"Prospects of high redshift constraints on dark energy models with the Ep,i-Eiso correlation in long gamma ray bursts","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:865a176ba6ad47d184b945c2920e195d8b22ed8b65a499cd398140051bc10f7f","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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:a160c652587470253d3d418fbe7b000e084965a5735f21beadbdf3688f597468","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":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.040035Z","title":"Model-independent distance calibration of high-redshift gamma-ray bursts and constrain on the ΛCDM model","venue":null,"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":"2026-08-07T04:56:34.919092Z","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:cb64f601ab6d6a49baac8f594b4d1e8a32c0deafdc71307456f39c0044fde406","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3847/1538-4357/aa8725","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T05:05:42.026285Z","title":"Model-independent Constraints on Cosmic Curvature and Opacity.Astrophys","venue":null,"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":"2026-08-07T04:56:34.919092Z","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":"sha256:4dd05abb43e404b57f681a228ce2a379584807c759335ff884349245ce2469ce","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 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T04:56:34.919092Z","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":"sha256:8c720f83e9647b36d5482d3ae43ae719ae3b8f04b987ae06b84ea0e7e18146ad","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":"2026-08-07T04:56:34.919092Z","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":"sha256:134bbe1b0b435bc452c6a7fa3877bc33b176cbf4f67df6ed3ced63c9de13e3d9","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":"2026-08-07T04:56:34.919092Z","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":"sha256:2e8affc8262c2803cfde1f4933ed744e706964c810f849cd0694fa96b75cc7ff","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":"2026-08-07T04:56:34.919092Z","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:f918378e048f3463081e6660ab8b3e83cc2ce462d239836616edc603493c204a","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":"2026-08-07T04:56:34.919092Z","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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 100 of 114 outbound references and 0 inbound Pith citation observations for arXiv:2506.08929."}