{"as_of":"2026-08-09T11:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f7388620592fb463344377bdbbaf6b9e23cd103d43ca9d9f69839b2e664a4d50","coverage":[{"denominator":37,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":37,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T12:54:20.734400Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T00:27:34.851414Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-18T05:10:54.401477Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.19397","snapshot_observed_at":"2026-08-09T00:27:34.851414Z","title":"cc/paper_files/paper/2021/file/ 312f1ba2a72318edaaa995a67835fad5-Paper","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.04406","last_updated":"2025-06-10T16:38:08Z","snapshot_observed_at":"2026-08-09T05:59:37.997741Z","submitted_at":"2025-02-06T09:23:06Z","title":"Calibrated Physics-Informed Uncertainty Quantification","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T00:27:34.851414Z"},"links":{"cited_paper":"/paper/2502.19397","citing_paper":"/paper/2502.04406"},"observation_digest":"sha256:4300396a39e31752711117f79f1006f6abea9c1988eb16263737dffabb71c85d","observation_id":"5a98b19f-4e72-4c2a-a092-aac273f5069d","resolution":{"observed_at":"2026-08-09T00:27:34.851414Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"cited_work":{"arxiv_id":"2502.19397","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.19397","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Modelling chemical reaction networks using neural ordinary differential equations","venue":null,"work_id":"fc5e573c-114d-4008-921c-0580cce046e9","year":2025},"citing_paper":{"arxiv_id":"2510.22104","last_updated":"2026-04-16T18:58:35Z","snapshot_observed_at":"2026-08-03T07:16:31.057125Z","submitted_at":"2025-10-25T01:09:59Z","title":"TRASE-NODEs: Trajectory Sensitivity-aware Neural Ordinary Differential Equations for Efficient Dynamic Modeling","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-18T05:07:45.633258Z"},"links":{"cited_paper":"/paper/2502.19397","citing_paper":"/paper/2510.22104"},"observation_digest":"sha256:7fc99f0bed5ee7dfc2755a0172feb8734ac30a7cba348025ac349b7fa1629fbe","observation_id":"59b7c8b6-4668-4c87-aebf-da7ccc13fd34","resolution":{"observed_at":"2026-05-18T05:10:54.403504Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.19397/citation-record","integrity":"/paper/2502.19397/integrity","json":"/paper/2502.19397/citation-record.json","paper":"/paper/2502.19397"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.150028Z","title":"M.; Waage, P","venue":null,"work_id":"53ff1479-dd92-4006-be7e-98e0f9485176","year":null},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.598340Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:57f6fff0df971166f9fc5ee6ed4ec9b82b12cbc0cd2d1b428f97c1d3d5b70004","observation_id":"4b74eb94-42c0-481f-b371-d699dfd782f8","resolution":{"observed_at":"2026-08-08T12:54:21.153701Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.138708Z","title":"Mathematical models of chemical reactions: theory and applications of deterministic and stochastic models; Manchester University Press, 1989","venue":null,"work_id":"e30cf9b2-9fbb-4d2b-af5f-3cef5e70d0d4","year":1989},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.602454Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:6a5d2bf525139cbedb73f566fa98f67800811c07d2aabef2db6c934d1f2437a9","observation_id":"e262fe0b-d561-4c07-ab67-107f23316ab8","resolution":{"observed_at":"2026-08-08T12:54:21.142712Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.127629Z","title":"A.; England, J","venue":null,"work_id":"eaa831d6-6c25-471e-9eff-50bf4a077b8b","year":2017},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.606193Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:2a7aeae3143c9a7b40ffbc190e75b5ccdee2242e573104c5c810fdf13db1ee33","observation_id":"ba012dca-7165-453a-bac6-6338436ff652","resolution":{"observed_at":"2026-08-08T12:54:21.131345Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.117201Z","title":"F.; Pathirana, D.; Fröhlich, F.; Hasenauer, J.; Banga, J","venue":null,"work_id":"93644ce9-a9e5-4aaa-97c6-53a73c2128bb","year":2021},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.609613Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:697c45bf7d84e4ccb4f91f62e658e9dc867d8721d54e6e4ec8f10ce7a504f34a","observation_id":"1fbd82d3-ec39-486a-a686-2005ef0873c7","resolution":{"observed_at":"2026-08-08T12:54:21.121006Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.106478Z","title":"T.; Weindl, D.; Hasenauer, J","venue":null,"work_id":"fc343fb6-61c7-47d2-bd1a-36274c78371c","year":2023},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.613669Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:601e14d87633c7e52507c2e144fc297c8a59399d9ee4cdfe8b5258036f06172a","observation_id":"8c6d2a8b-62ae-4518-83b1-ed458cc9f117","resolution":{"observed_at":"2026-08-08T12:54:21.110192Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.095346Z","title":"Linking data to models: Data regression","venue":null,"work_id":"f3921dad-e757-4e33-bada-a2cacdbd32db","year":2006},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.617398Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:a5ad6d23dc0d85356166b51970146518bb8b3da0232d72cf27bb7b2ffd02e963","observation_id":"b4e5a148-3906-455b-9cc3-e1a5c5ea4e02","resolution":{"observed_at":"2026-08-08T12:54:21.099486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.084043Z","title":"Competition for catalytic resources alters biological network dynamics","venue":null,"work_id":"fd9df41d-f52b-4ad7-937d-f8e52e4e7bb8","year":2012},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.621076Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:f3278707fb7877a6fc0263a052b06f1b3a91663fc3e3e6fa0160a257f511a025","observation_id":"ead6d224-b5b4-45da-8cb9-7eeaaadce7a7","resolution":{"observed_at":"2026-08-08T12:54:21.087782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.073513Z","title":null,"venue":null,"work_id":"9ab47f94-662f-4b5c-b2eb-9a54fadc9078","year":2023},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.624841Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:3b08c87e87bd500861ecc2bc9da5be575f5c0b78821f72126005d911177854a5","observation_id":"2ec48ebb-1b72-49ea-8a1f-0c1306b1a5b2","resolution":{"observed_at":"2026-08-08T12:54:21.076860Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.063094Z","title":"T.; Rubanova, Y.; Bettencourt, J.; Duvenaud, D","venue":null,"work_id":"703093eb-07d7-44ae-8558-f277b0d056bb","year":2018},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.628497Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:a6943aadbf9cedbcab255c096332fba089e4727e7442b9f406e7078d94672623","observation_id":"8c83eb00-7f35-4808-80ce-a99a26cc8c13","resolution":{"observed_at":"2026-08-08T12:54:21.066655Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.052614Z","title":"Deep learning","venue":null,"work_id":"9061756f-d534-4979-b826-cf58c07f05f4","year":2015},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.632219Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:5ea8d08c79913b267462fc0ed77fccba5da8d5ccab1da0cb99d5eafb7d3250cd","observation_id":"7d2d1b98-90d8-445c-9259-6b47b33448d3","resolution":{"observed_at":"2026-08-08T12:54:21.056343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.041318Z","title":"V.; Azevedo, P.; Cardoso, V","venue":null,"work_id":"fc272419-62a3-4d53-82c4-663b18b76027","year":2021},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.635742Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:afac1e8216b2601852e87b9de9e8f505235d4007b51e07a5b0ad4fe124714c0c","observation_id":"d06b60c7-a13f-4de0-881d-57e5c9a94201","resolution":{"observed_at":"2026-08-08T12:54:21.045038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:54:20.641278Z","title":"D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; others Language models are few-shot learners","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.641278Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:633c66e0a8ca1b6cc83b2741a97039e68381a43cde22bebafed3757cf961e132","observation_id":"1e36366e-0ad1-4b30-8ce4-94f3476ca840","resolution":{"observed_at":"2026-08-08T12:54:20.641278Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.023491Z","title":"E.; Bachrach, Y.; Huck, W","venue":null,"work_id":"5ab22a9f-f682-413d-98a2-e63d450f5011","year":2023},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.645016Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:0aba8914ad7ca3721817e2643581a9a87876acf4c7716ad07008c5c866c53416","observation_id":"2ccbbc00-b116-40bc-83b4-6f814a31f78b","resolution":{"observed_at":"2026-08-08T12:54:21.027503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:54:20.648526Z","title":"nature 2021, 596, 583--589","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.648526Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:d5ddab7c65d15870a6569570814e9e0e2651ab8b935c57a5c58bbb9e5dee2fbc","observation_id":"79fac0f8-9008-4679-9dee-3ab7c0a227ab","resolution":{"observed_at":"2026-08-08T12:54:20.648526Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:21.005614Z","title":"Multilayer feedforward networks are universal approximators","venue":null,"work_id":"cf52a1ec-4c11-4cd5-8c03-df674950e1c8","year":1989},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.652180Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:044d9f3f62168440baccffaf3833f315a07c03da0a54a4892607d409fc46cb1d","observation_id":"140b5bc9-0c46-45ca-bf1a-a7a75beae6d0","resolution":{"observed_at":"2026-08-08T12:54:21.009547Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-08T12:54:20.655758Z","title":"Deep Residual Learning for Image Recognition","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.655758Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:245fc7232b8711a7d8d0f722cb63338e789100a89f642b81586a2b8340e3a00c","observation_id":"e7049d53-5605-46ce-a6d1-a531927cfc29","resolution":{"observed_at":"2026-08-08T12:54:20.655758Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1605.08803","last_updated":"2017-02-27T23:21:10Z","snapshot_observed_at":"2026-08-06T07:41:35.475664Z","submitted_at":"2016-05-27T21:24:32Z","title":"Density estimation using Real NVP","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.08803","snapshot_observed_at":"2026-08-08T12:54:20.659462Z","title":"Density estimation using real nvp","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.659462Z"},"links":{"cited_paper":"/paper/1605.08803","citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:2d1f4c03f10ee92b46a9b0de2511ec76531ee5f5c68e700ea5e034c7d760222c","observation_id":"2814c02c-dd14-47d8-a968-b6e0f3ce4c01","resolution":{"observed_at":"2026-08-08T12:54:20.659462Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.988272Z","title":"Neural Controlled Differential Equations for Irregular Time Series","venue":null,"work_id":"ff0eabf9-b3f5-40fa-aab2-8ac9b7205e41","year":2020},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.663580Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:052749ef7e00ca1c2bb511bbb8d8b7c6d12372b8da72b0b4f0515e5a9b1e6536","observation_id":"9e91da3b-06f9-46f8-bb35-5e7b9426b310","resolution":{"observed_at":"2026-08-08T12:54:20.992126Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.977899Z","title":"Autonomous discovery of unknown reaction pathways from data by chemical reaction neural network","venue":null,"work_id":"fab92f12-aa46-495d-9d10-5627928dc5f1","year":2021},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.667436Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:0df43fdaf6666fc31861ef31cef10c1c6e07eb3ea93eca86708a0dc72d121364","observation_id":"05591eb9-b305-4708-8cde-cff5c505c169","resolution":{"observed_at":"2026-08-08T12:54:20.981552Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.966903Z","title":"ChemNODE: A neural ordinary differential equations framework for efficient chemical kinetic solvers","venue":null,"work_id":"659a41a5-656a-4559-b123-c1ae528110e3","year":2022},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.671583Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:f3b71440f833caa81fbfb8d6790b3f3d11cd5991816ef56ca365839270c634fe","observation_id":"bdae733d-45a0-43d9-b005-69e79e29414e","resolution":{"observed_at":"2026-08-08T12:54:20.970616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2001.04385","last_updated":"2021-11-02T12:06:44Z","snapshot_observed_at":"2026-08-08T03:57:24.280922Z","submitted_at":"2020-01-13T16:40:35Z","title":"Universal Differential Equations for Scientific Machine Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.04385","snapshot_observed_at":"2026-08-08T12:54:20.675332Z","title":"Universal differential equations for scientific machine learning","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.675332Z"},"links":{"cited_paper":"/paper/2001.04385","citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:713f73de0c878a4b7f15c028de103cbf72b57f688531c410e6a68ecec7fd350d","observation_id":"52af010d-8922-45ac-99bf-57f540293bea","resolution":{"observed_at":"2026-08-08T12:54:20.675332Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.956987Z","title":"R.; Runikhina, S","venue":null,"work_id":"ced43d43-2c43-4745-9aa0-c1ca22460798","year":2023},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.679424Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:f8380c58ce26a871d68a260242c3c41b08ed6437f4f03a2776a6779dad9ff2f1","observation_id":"58868d7d-74e1-4e46-b616-f658a3ec4a17","resolution":{"observed_at":"2026-08-08T12:54:20.960308Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.946951Z","title":"A.; Essex, C","venue":null,"work_id":"8e52c32e-e8b9-4deb-8042-e4f466d59ba6","year":2013},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.683275Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:e074a8b881821a7a5ba6b9495444844764359d9efdfc9cde69d8ff4106932d12","observation_id":"c2fe9293-4fa9-4227-b323-8d3c0f8b6f20","resolution":{"observed_at":"2026-08-08T12:54:20.950546Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.936762Z","title":"Singly diagonally implicit Runge--Kutta methods with an explicit first stage","venue":null,"work_id":"7d5b6279-3893-442c-8f70-98fe9d8e6457","year":2004},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.686882Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:cc67c4fc2d8cb39579c42e5fe2ceb18c5f5798f79f16ea391da6a762d44d1a42","observation_id":"c0a3106c-72db-4b0a-b68f-240dbbc677b2","resolution":{"observed_at":"2026-08-08T12:54:20.940436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.927438Z","title":null,"venue":null,"work_id":"7bfabced-fc19-4732-b6b6-8a0ade35b9dd","year":2020},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.690652Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:d5516256f67e0fd3d5cfd7339e80c47750fc98e8979622b685b726196d3dfc78","observation_id":"419c85dc-0e74-47ee-901e-5d9b1e2ef611","resolution":{"observed_at":"2026-08-08T12:54:20.930750Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.917324Z","title":"E quinox: neural networks in JAX via callable P y T rees and filtered transformations","venue":null,"work_id":"3dac04bc-d7bd-4380-8a3e-6f6ce40694fb","year":2021},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.694467Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:99f1faec7e4b59cfdb4aa329a63f753d010bd103611fc87d4072e405bebfb817","observation_id":"353e0404-fd70-407b-8179-cb00e5e4db17","resolution":{"observed_at":"2026-08-08T12:54:20.920968Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.907482Z","title":null,"venue":null,"work_id":"f8f7622a-b1e9-4f4f-91e9-552edaf83afb","year":2020},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.698284Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:9adceb93f7148955adcfc5a814bf6daf5af67703ba17e099ee75a7f5dba4e54d","observation_id":"a5aa5064-30ad-4e1e-b207-8a860197ef10","resolution":{"observed_at":"2026-08-08T12:54:20.910934Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.897150Z","title":"Distilling Free-Form Natural Laws from Experimental Data","venue":null,"work_id":"6f7ab922-708c-4232-bde8-88bb839e4ddd","year":2009},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.702164Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:c2a43e8b37c670c6a47d3454b1f70aeaedc8635da2250dcd03ca0e68fbeddb82","observation_id":"4438ce77-f54e-4c2e-a2f7-9ea3318794ce","resolution":{"observed_at":"2026-08-08T12:54:20.900799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.886558Z","title":null,"venue":null,"work_id":"64b43c1c-d60e-479c-b2a0-7bb328fbea75","year":2006},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.705894Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:ff0634154726682650c790506b933206704f5976f6cab7779e7a892de0438846","observation_id":"627fde12-2780-4f10-9150-4110089bea9a","resolution":{"observed_at":"2026-08-08T12:54:20.889993Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.875884Z","title":"Long short-term memory","venue":null,"work_id":"3330e62e-4f05-449e-8142-cc9152d1a00f","year":1997},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.709568Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:b1cfb39dec9493f1b411bc13f06184dd4bfe85e8c245cebef891c6df75491ffe","observation_id":"1ba09363-16d3-482a-9a27-66c90d60d38b","resolution":{"observed_at":"2026-08-08T12:54:20.879574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.862616Z","title":"A Tutorial on Chemical Reaction Network Dynamics","venue":null,"work_id":"9bdc9b1d-7aae-48f2-9716-d4edbfd7f087","year":2009},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.713233Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:403a044c282eac34aef33fdc8f687b004192346a1f7d6d5221b3785bec922d58","observation_id":"6093cf7a-0f5c-4e96-b382-1f562a51758c","resolution":{"observed_at":"2026-08-08T12:54:20.867268Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.849656Z","title":null,"venue":null,"work_id":"0e28a5d1-7243-43db-a0ab-ff38887da04f","year":2006},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.716827Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:2ced871c34df3333309a0b5ef4e5be1690b2ce43b884d6f144581c95ac21abc5","observation_id":"674dfe36-5d4a-4840-bc73-34f3f2a535e1","resolution":{"observed_at":"2026-08-08T12:54:20.853493Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.837663Z","title":null,"venue":null,"work_id":"39e1cf82-71ea-4564-b732-8643825dead7","year":2009},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.720356Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:7b1f3b33064973e0c86fed2cc03f79db5e50ceb382f81749eafdf0db4fdc77b9","observation_id":"06a7c5f6-1a7f-4350-8d83-c48b374624f0","resolution":{"observed_at":"2026-08-08T12:54:20.841576Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.824859Z","title":"Beyond finite layer neural networks: Bridging deep architectures and numerical differential equations","venue":null,"work_id":"9c6bb709-21a8-4b35-9428-269627f00af6","year":2018},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.723676Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:68019141ebce8f35af6daf6bbe94ba534371651b940bafe6b13d6a0c9090321a","observation_id":"4c85ebd7-d663-477c-a099-02ad651f1dd5","resolution":{"observed_at":"2026-08-08T12:54:20.829275Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.813573Z","title":"O n N eural D ifferential E quations","venue":null,"work_id":"f9859250-c3d4-4e47-88c2-6abc98b71e4f","year":2021},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.727324Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:6532b413312c9a383fa24ec3e7dbf358faeb31fda0c0ee05ce7a72420806fde4","observation_id":"e1300499-bcbf-4b6d-838e-9e0cc88be0c5","resolution":{"observed_at":"2026-08-08T12:54:20.817261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T12:54:20.801182Z","title":null,"venue":null,"work_id":"5af396b7-3996-45ca-b978-7208c72c6de4","year":1996},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.731078Z"},"links":{"citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:f5ce6b2c12a1dd96e0a008b239d436996f6767ab2bd5769cddd4ec944d260175","observation_id":"9d7cbc55-a131-4e08-aa36-fb8b4cddc142","resolution":{"observed_at":"2026-08-08T12:54:20.806072Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05573","last_updated":"2023-10-09T09:54:12Z","snapshot_observed_at":"2026-08-05T15:45:14.236317Z","submitted_at":"2023-10-09T09:54:12Z","title":"ODEFormer: Symbolic Regression of Dynamical Systems with Transformers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.05573","snapshot_observed_at":"2026-08-08T12:54:20.734400Z","title":"A protocol for dynamic model calibration","venue":null,"work_id":null,"year":1977},"citing_paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-08T12:54:20.734400Z"},"links":{"cited_paper":"/paper/2310.05573","citing_paper":"/paper/2502.19397"},"observation_digest":"sha256:b3abb71e0f50f60d22e8d236ecaa80c043c7176ba88f306654bca7e5ec6faf53","observation_id":"8ef536ea-2501-4cbf-83b0-964f4aaa4e7b","resolution":{"observed_at":"2026-08-08T12:54:20.734400Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.19397","last_updated":"2025-02-11T10:10:33Z","latest_version":1,"primary_category":"q-bio.MN","snapshot_observed_at":"2026-08-08T12:46:04.220940Z","submitted_at":"2025-02-11T10:10:33Z","title":"Modelling Chemical Reaction Networks using Neural Ordinary Differential Equations"},"reference_resolution":{"displayed":37,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":13,"verified_exact":0,"verified_fuzzy":24},"total_outbound_references":37},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2502.19397."}