{"as_of":"2026-08-14T13:10:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9bd21008c5a0127d807a5f31c90a3133ebeacc2ae31a154682ca0a227b997680","coverage":[{"denominator":55,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":55,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-26T09:26:31.874792Z","state":"measured"},{"denominator":55,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":55,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2606.23757/citation-record","integrity":"/paper/2606.23757/integrity","json":"/paper/2606.23757/citation-record.json","paper":"/paper/2606.23757"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T09:26:31.874792Z","title":"Proceedings of the national academy of sciences , volume=","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:784edbac90b2aaa42c2edb4471da0bc4a4ef410c9249f764115907bb6f3f0b46","observation_id":"d6778600-c21b-4c34-85a9-5251e90e26b3","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Science advances , volume=","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:12fef5289a5db8f312775ca303e26904cef8fc365b8ab5f72925e49133a4c13d","observation_id":"1acc19d4-af26-41b9-b9ea-c7ac79e1c7a0","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"science , volume=","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:c53ac9e6115fbebfcabbcf68121c31c33cc63354068871e71094f42622b19043","observation_id":"ea1134d7-b9f9-4544-91ea-13a8b74e6fc0","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Proceedings of the National Academy of Sciences , volume=","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:780aabe7f8d82acdf3d52e1bfb7b5a2aa4d4fc1586c6de59a081d4632bae4df2","observation_id":"67c72611-d03f-41d1-95d9-88e429389284","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.01582","last_updated":"2023-05-05T17:44:07Z","snapshot_observed_at":"2026-08-11T11:36:50.605147Z","submitted_at":"2023-05-02T16:31:35Z","title":"Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl","version":3},"cited_work":{"arxiv_id":"2305.01582","doi":"10.3399/bjgp20x708941","metadata_source":"pith","pith_arxiv_id":"2305.01582","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl","venue":"astro-ph.IM","work_id":"666d4dea-669b-423b-9bd4-e14eee78fcb4","year":2023},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"cited_paper":"/paper/2305.01582","citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:9e47b9f22d30ad2037d01aebe402b30d25785d1751b445e8e1373ec9335c88ce","observation_id":"91decafc-5a48-4e1f-81e9-8985652cd6be","resolution":{"observed_at":"2026-07-04T09:49:44.676100Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T09:26:31.874792Z","title":"Proceedings of the 2020 genetic and evolutionary computation conference companion , pages=","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:2c7dfe87266e5743e0ceaeaf99fa5f45be4ee4191f8b09e24170c7c1be0869d9","observation_id":"ca50d119-703d-496d-8350-5594e826a961","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2006.04732","last_updated":"2020-06-08T16:38:15Z","snapshot_observed_at":"2026-08-13T22:29:17.124763Z","submitted_at":"2020-06-08T16:38:15Z","title":"A Semiparametric Approach to Interpretable Machine Learning","version":1},"cited_work":{"arxiv_id":"2006.04732","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2006.04732","snapshot_observed_at":"2026-07-04T09:49:44.677245Z","title":"arXiv preprint arXiv:2006.04732 , year=","venue":null,"work_id":"8c332ef0-9302-42d2-bf7d-7d8934a9092a","year":2006},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"cited_paper":"/paper/2006.04732","citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:927d0fc8bd53f3c68f95e2c33871b54e993e48544cd4d8439b3e2b95b438895e","observation_id":"16a35ed9-db4d-46e7-b62e-25a749093675","resolution":{"observed_at":"2026-07-04T09:49:44.678496Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T09:26:31.874792Z","title":"Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=","venue":null,"work_id":null,"year":1996},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:da5dc999f5ae28a3f2b24c086e5eccaff8b1e75feadbc6ad353de64214ee693a","observation_id":"d374a4c3-4650-447a-933e-344997adc34a","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Journal of the american statistical association , volume=","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:390313525bb415f195c1d159761b6e465b10059c9a3a8ee567b5fea839f51ed8","observation_id":"52c7daaa-c0c7-4b03-951f-2a15a535e40b","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:611f7dcfbd82af88651d5792f52ecbdbb3e83f72c3e3ce15d89918dde473b3b4","observation_id":"aaaffd51-5c77-4629-b31b-b689afa031a8","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Chemical Engineering Science , volume=","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:5cb0e923e73952a54514e28ad8359ee0d52988ff98a8fe08a49120d1659df2f6","observation_id":"70bfd993-1603-4f32-80e0-daaee480658d","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Computers & Chemical Engineering , volume=","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:e5b001069378058a644b6cb45921c8a57673deea48a41e6aeb2b070381452e23","observation_id":"8bb786d8-293a-4296-815d-f54593b8529f","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Chaos: An Interdisciplinary Journal of Nonlinear Science , volume=","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:c32412300e92033c375545e1f8148ab38068b56dce1661aaef37612e8a7956d8","observation_id":"b0004b1a-17f7-436b-83b6-a23e756bb9ad","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Chemical Product and Process Modeling , volume=","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:589ffa0919e476ade05c5285a7f93a9f01cffb7856382cb9a4a6c49a58e20748","observation_id":"01a5d380-482c-4084-a0b1-2cb5b8cf4aaf","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"PLoS computational biology , volume=","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:52c5b0d7b9dcfa37fd9d2cf3d7dbeed76664c35c3427f29ae9341a1e2abe4a17","observation_id":"1802a39c-90f3-44b1-979f-b9204cc8507d","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Chemical Engineering Science , volume=","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:a5752c15d6d2d3426291c120842c37461cc113b0f112721ce51e885c324e3fbc","observation_id":"96ba07e0-b420-4741-99e9-96d29c031b7c","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Journal of the Royal Society Interface , volume=","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:a05cbe68fe4f783a79528c60a50f87f9f44f14408aeba896370de3686d1e6f2c","observation_id":"d950ad4b-0b3d-4d24-8029-affda83227dc","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"IEEE Transactions on biomedical engineering , volume=","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:3a1654360532a54220ab84a7c0d470bf661b47b764e3a591edd7c71429eb0a8c","observation_id":"2bc74242-316d-4787-a93d-915a223e47e8","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"(No Title) , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:3d22d9e545e45000a9293f30e83eb7c76692cec272a16549f01dc2a41aaa7e2d","observation_id":"f29bb3f9-90df-4ec6-bc32-3d27f62f957d","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Chemical Engineering Science , volume=","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:7f3e40c5be237bc327d2c1593e27511d378cc6d1fbe7f382e397a0dc69b9c56a","observation_id":"2a7f9c24-1015-4a23-8647-e9a6c5ca4c9a","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Archive for rational mechanics and analysis , volume=","venue":null,"work_id":null,"year":1972},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:a6f9bae2d460fea0a806f36212e8b2d0b21f761c164e626581149898aabb47e0","observation_id":"d34bc244-1193-4e3e-87c2-f09ca0b2a23e","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"The deficiency zero and deficiency one theorems , author=","venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:d8e6cc19effafc1bb492473544bc173d100aaf553928a9aa0458c186702d3c11","observation_id":"d5ff790d-108f-4386-a978-18089df27849","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Biophysical journal , volume=","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:5ca8770c0f1e040c9ac73caa00c96361a9d0d82cae892d31517363ded309664e","observation_id":"635bed17-c11b-4c6b-96d7-7aa0baa96b1b","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"BMC bioinformatics , volume=","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:bcdedbdfd5fbe0824317c0678c381773f33a1a4919023631e7b3e1fb60ff2764","observation_id":"bc5f688e-502c-477c-8160-7322da97241f","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Wiley Interdisciplinary Reviews: Systems Biology and Medicine , volume=","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:d000fe1dec6e9cfdd876410ffe05857569f3681e1d22f187dd29c650f4fec422","observation_id":"29aeae0d-1a5e-4349-9d4a-c42e80b6589d","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"The Journal of Physical Chemistry B , volume=","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:4901af4ee72ad7b81268c997b1d4ee6522d0077536000130b73bc6c5acf8f89a","observation_id":"ec4756ae-10e6-41d3-a7ac-524f01bc48f9","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Journal of Mathematical Chemistry , volume=","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:ef9929596d754218199a509cbe415dfede7c3a44e3de195d0cef34d46f1c9392","observation_id":"59d68baf-0d2f-4ade-bac8-8aefe187c48d","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"ACS central science , volume=","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:aea960e09e48d7f45b0407df42ad919a7766f7f04c8934d74d54ef7406a91a70","observation_id":"3dd86d3b-3f83-430a-8462-613d4ebb8bd2","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Journal of Chemical Information and Modeling , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:bb59a14991946c4f9858bcc35f5ddd182d7dde2c43518727674877f074b94134","observation_id":"bbbd9131-8ca9-48d8-997a-a722577fd909","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Nature , volume=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:633e70e153e6b91f8cc5741800225e7ab6b3af56d347f518cc8f28175345a09f","observation_id":"030e8d56-2eea-45d7-a393-88f4e409fd6c","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"2006 , publisher=","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:baf9250f3add32c0e784507f60b1cbf2d7a8872fa50b1bc53a766d02ebd8c552","observation_id":"81483525-39b3-4cc8-b223-fce5e95a4d08","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology) , volume=","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:d21e6b043f5d10fc2e7efdaa7d32e8a8b044ee67be424e9a8771535b574071b4","observation_id":"2afbe0bb-a259-40fe-b1ee-96a24b2c5705","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Journal of Computational Physics , volume=","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:c0f2eb4ee29c2681c87719b0502ff8ecae70c079b15b57a2b5595165a4f0ac9b","observation_id":"6befd61c-0dc0-4c92-adde-ac82798756bc","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.04591","last_updated":"2020-10-09T14:18:31Z","snapshot_observed_at":"2026-08-13T21:22:58.997686Z","submitted_at":"2020-10-09T14:18:31Z","title":"Physics-Informed Gaussian Process Regression for Probabilistic States Estimation and Forecasting in Power Grids","version":1},"cited_work":{"arxiv_id":"2010.04591","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2010.04591","snapshot_observed_at":"2026-07-04T09:49:44.672684Z","title":"arXiv preprint arXiv:2010.04591 , year=","venue":null,"work_id":"75f33431-29a5-4b40-8703-d53707eaa4cb","year":2010},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"cited_paper":"/paper/2010.04591","citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:1bded4bba28dc84167231fc69dec812734cf4a1efb203b91c330ba84cdde431f","observation_id":"17c26206-8d47-419a-8581-7e8a030fcfb1","resolution":{"observed_at":"2026-07-04T09:49:44.673911Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-06-26T09:26:31.874792Z","title":"2016 , publisher=","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:e61e4b03524313c11908d78f612f421da8b33e2101897b95477a5888fa2f14ac","observation_id":"3d960664-1b24-4c83-9912-00b9d2efd5d7","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Annales de la Facult","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:4fb4a7fb95b8549ca767d720f29d39a162e60c49ec7f5b5e2b9bbd96ed5b7323","observation_id":"f3bed395-d48c-4e49-9584-2bdf7be83b90","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Data-Centric Engineering , volume=","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:8ccc2a3f94fefec07016e2ec09f0673f5b1404a1c4cf9fac366a72d6d8176d56","observation_id":"ea5b07fe-3a32-48cc-9183-0c9025976d4d","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Journal of Computational Physics , volume=","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:d9a443e64b72962e7e56c9e7cae0615995154af8a156aadb8c1520262cb9a4f8","observation_id":"3803c461-d2c0-44e0-9623-e9a5d59ecfc3","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"IEEE Transactions on Control Systems Technology , volume=","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:109bf01555057136a813f187cca21976391cb60a1632afd2e9c5b5f949162799","observation_id":"a1672377-9c35-4c8a-9c71-3d20e5562311","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Automatica , volume=","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:b4245b60b3d704e785c45fcf26c4d02dd5c5a1e553ad4d1de320094c5ecb7763","observation_id":"877897b9-f109-4449-8cb1-7e33aa36ff9f","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"The journal of chemical physics , volume=","venue":null,"work_id":null,"year":1953},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:af94025252bb0250f069f331ce6bf045c58356e6d6880a4d057c720527263785","observation_id":"588070a3-6d10-4658-9813-d223c8975f0b","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Biometrika , volume=","venue":null,"work_id":null,"year":1973},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:3264da3b8d55b21871db3d6ca6b7ca1484c3c607b53e1025abacaf0116f4cf74","observation_id":"2b471cb9-75d7-404d-a553-2fc1b767f483","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Journal of the Royal Statistical Society Series B: Statistical Methodology , volume=","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:e6c344de4c6cb2993a43bb93370d121353c737201579c66c87af2b7cd4e711c2","observation_id":"56fc7992-7aed-4d51-857b-8919c60ec40f","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Artificial intelligence and statistics , pages=","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:8f0e3f0302214c5cbd4259bb499b2fcab7bab79403922fef58ad7a05101a2154","observation_id":"d72d04c4-cb28-4932-adab-e09fb814b21e","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Journal of the american statistical association , volume=","venue":null,"work_id":null,"year":1988},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:5729f390de50e7f238a3d0b1a2140e2f4eded5b2cea8669ee3903a49c47efa48","observation_id":"2ca3b43b-c425-45aa-b192-a36ff83d923e","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Journal of the American Statistical Association , volume=","venue":null,"work_id":null,"year":1993},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:c6bdb85e3f45bf8ea7bcbb03cbb2a1dcaeea5035fc110ce18e2f23b414a7d8ee","observation_id":"6a096b35-f3cb-461c-b1d1-25aec1468d5b","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:52876ee949d47addd3a40c91d80a7a8fa7bce2b6a3bc758b4636283c997e218f","observation_id":"8273ad46-dd02-4b01-b66f-d1be42e0b14d","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Journal of Global optimization , volume=","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:acf705cba86788ddea99410a6856c7136f266cf153e915d6ced6d5d076106e91","observation_id":"f1df6b11-0673-4a48-8386-a3107e89602e","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Advances in neural information processing systems , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:eaa7995bada0da7c7005103c60736d8e6dbcc87966317981c450e1b32de8b9e2","observation_id":"d33171a2-2bc7-4bbe-8e67-e1b930c97c2c","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Proceedings of the IEEE , volume=","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:55571f7576f8750534f1fc0814c4a6c53ef5799e60cd2d91d65e9dd8bdff151b","observation_id":"2f14edf9-56ae-4f74-94a1-99c49866078c","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"European Journal of Applied Mathematics , volume=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:e7bd6e4292d095ae1cf9cbb8fc7afd13a1423ae2ea39f46288931d4e04565850","observation_id":"2ce9192a-f8df-4630-98c5-039393672a36","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Neurocomputing , volume=","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:0714dbd27d9e02bdc11d4b8710f576a1686c0b7ff7ff1ce233dc1d1af5d5cd5f","observation_id":"a4a78d80-b376-46ed-ada7-f5feb19c384f","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Journal of Machine Learning Research , volume=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:7b8f567616063c6f47c832ab8b0321f62647c04e0ba7e192f0a5aaa9fdcb9322","observation_id":"a77232f1-7b36-4727-ae62-9d55a56bf035","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Chaos: An Interdisciplinary Journal of Nonlinear Science , volume=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:808f3668e2ed291ea8f1041535975d71f32fab86c7eaf7e282cb67d642333b57","observation_id":"e72bf708-e0b2-4295-9341-d88a770d2b29","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","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":"2026-06-26T09:26:31.874792Z","title":"Energy and AI , volume=","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-06-26T09:26:31.874792Z"},"links":{"citing_paper":"/paper/2606.23757"},"observation_digest":"sha256:da937f3cfd0305b895ab96aa67608f9e65ceeb3c08c09c05ad8752f0f371f2af","observation_id":"d634ecba-8fa6-441b-848e-84b4f16ae1b7","resolution":{"observed_at":"2026-06-26T09:26:31.874792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2606.23757","last_updated":"2026-06-22T08:43:04Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-07-06T23:58:25.400508Z","submitted_at":"2026-06-22T08:43:04Z","title":"Synergizing Physically Constrained MCMC and Chemical-Informed Gaussian Processes for Reaction Network Discovery"},"reference_resolution":{"displayed":55,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":52,"verified_exact":3,"verified_fuzzy":0},"total_outbound_references":55},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2606.23757."}