{"as_of":"2026-08-20T20:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:50f941088a53a1cc297de2d6ad11fd3d5db5c60dfa958e592c00606f17ee1e16","coverage":[{"denominator":12,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":12,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T15:13:40.597453Z","state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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":"2026-08-14T06:06:29.318154Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-08T14:54:06.468101Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1908.01786","snapshot_observed_at":"2026-08-14T06:06:29.318154Z","title":"Stochastic data-driven model predictive control using gaussian processes,","venue":null,"work_id":null,"year":1908},"citing_paper":{"arxiv_id":"1909.00178","last_updated":"2020-03-08T10:18:50Z","snapshot_observed_at":"2026-08-20T12:23:48.933726Z","submitted_at":"2019-08-31T09:47:02Z","title":"Learning self-triggered controllers with Gaussian processes","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T06:06:29.318154Z"},"links":{"cited_paper":"/paper/1908.01786","citing_paper":"/paper/1909.00178"},"observation_digest":"sha256:805a2c0ba42c285474c29216d0b6083a33c813cce197c90c6b43377b535c2e2b","observation_id":"dbaa8dc7-7e91-48cd-b34e-58e24eec4ae6","resolution":{"observed_at":"2026-08-14T06:06:29.318154Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"cited_work":{"arxiv_id":"1908.01786","doi":null,"metadata_source":"pith","pith_arxiv_id":"1908.01786","snapshot_observed_at":"2026-08-08T14:54:06.468101Z","title":"Stochastic data-driven model predictive control using Gaussian processes","venue":"math.OC","work_id":"68fc901e-fb24-4313-baee-f7d732073979","year":2019},"citing_paper":{"arxiv_id":"2502.06645","last_updated":"2025-02-10T16:35:08Z","snapshot_observed_at":"2026-08-20T03:09:03.629628Z","submitted_at":"2025-02-10T16:35:08Z","title":"Koopman-Equivariant Gaussian Processes","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T14:54:04.932786Z"},"links":{"cited_paper":"/paper/1908.01786","citing_paper":"/paper/2502.06645"},"observation_digest":"sha256:0b137660bca7119e47e3adbaba5f7c82c8fc6adf65f464e3917244fff5a3522c","observation_id":"da7a8d3e-9beb-4f39-b6ae-76047b1567a2","resolution":{"observed_at":"2026-08-08T14:54:06.472861Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1908.01786/citation-record","integrity":"/paper/1908.01786/integrity","json":"/paper/1908.01786/citation-record.json","paper":"/paper/1908.01786"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T15:13:40.688359Z","title":"2413–2419","venue":null,"work_id":"d1849180-1fc8-4f5a-b777-a324c7f26b31","year":2003},"citing_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:40.581593Z"},"links":{"citing_paper":"/paper/1908.01786"},"observation_digest":"sha256:97c2aa6206607e9b0f326fd2fbcdf67bfdc2f6eb1a6805c7013bcb96845c4ac2","observation_id":"805d029c-5b4d-4ab2-b467-5ad06eb40261","resolution":{"observed_at":"2026-08-14T15:13:40.695045Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-14T15:13:40.670956Z","title":"Evaluation study of an eﬃcient output feedback nonlinear model predictive control for temperature tracking in an industrial batch reactor","venue":null,"work_id":"5e30885c-8111-4e20-9f88-396f4106cf5b","year":2018},"citing_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"reference_index":1786,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:40.586808Z"},"links":{"citing_paper":"/paper/1908.01786"},"observation_digest":"sha256:2d3ccedb9ef7308b10873a52cb70c7a4e5dbf3e7ec8b052c954fbe6666d12693","observation_id":"639ff17c-6105-4cd6-8792-b6591682b4fb","resolution":{"observed_at":"2026-08-14T15:13:40.676335Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-14T15:13:40.834618Z","title":null,"venue":null,"work_id":"30c1e4b8-f991-4054-b0c7-838f21fa4657","year":2015},"citing_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"reference_index":1982,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:19.816984Z"},"links":{"citing_paper":"/paper/1908.01786"},"observation_digest":"sha256:4cfd9bf3ec47d4e6c48507ab274ead3f8cde1479235f50952de8d57fd3a4cd3f","observation_id":"152f5bd9-6ac6-4c3f-8c20-b08a94e30249","resolution":{"observed_at":"2026-08-14T15:13:40.839743Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-14T15:13:40.816994Z","title":"Economic Stochastic Model Predictive Control Using the Unscented Kalman Filter","venue":null,"work_id":"3d06736d-1dd2-4a9f-99b8-72c3bd2ca0bf","year":2019},"citing_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"reference_index":1991,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:40.536723Z"},"links":{"citing_paper":"/paper/1908.01786"},"observation_digest":"sha256:2503ecf775c2e617077c0d90f425547a1dc46644e7684cb96a6e7effa81463b2","observation_id":"7f4efb1b-aee6-4603-8696-c851b225d746","resolution":{"observed_at":"2026-08-14T15:13:40.822807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-14T15:13:40.748995Z","title":"2214–2219","venue":null,"work_id":"0c77088f-9b6d-493a-9216-72e7a48f6372","year":1951},"citing_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"reference_index":2004,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:40.565280Z"},"links":{"citing_paper":"/paper/1908.01786"},"observation_digest":"sha256:7ec43af6b2fb1d2c86456e0d387c3565158bc5bbe7645472bd44d20282f1871c","observation_id":"4d60fac3-ea6c-4cc6-8d60-abed93d54a61","resolution":{"observed_at":"2026-08-14T15:13:40.753876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-14T15:13:40.652790Z","title":"Optimal operation strategy for biohydrogen production","venue":null,"work_id":"387111c9-4191-4153-86b4-d364d3537c00","year":2015},"citing_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"reference_index":2006,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:40.592204Z"},"links":{"citing_paper":"/paper/1908.01786"},"observation_digest":"sha256:17c5ce7b8ea72600fb752405317d463e6f001a0575af4ed4b0f639d855836ae1","observation_id":"8acb5fe9-3588-4f5f-bbf9-5aa05b0350a9","resolution":{"observed_at":"2026-08-14T15:13:40.659355Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-14T15:13:40.781756Z","title":"2340–2347","venue":null,"work_id":"e4e0664e-1f0c-4a36-b440-c508b2b24fe5","year":2018},"citing_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"reference_index":2007,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:40.551372Z"},"links":{"citing_paper":"/paper/1908.01786"},"observation_digest":"sha256:62d47fc10e4ab82495b17ee073d38b38a48280caf4c87eb03c4f160facb85c21","observation_id":"a46909af-ac6e-413a-827a-9eb2feb84fac","resolution":{"observed_at":"2026-08-14T15:13:40.786929Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-14T15:13:40.710815Z","title":null,"venue":null,"work_id":"99b97031-70c0-4420-b448-b95dc30b5016","year":2018},"citing_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"reference_index":2013,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:40.576171Z"},"links":{"citing_paper":"/paper/1908.01786"},"observation_digest":"sha256:3698578d47685136bfe4f8ed05fcb7cfba23e7d97622cfb8161e74d0054f81dc","observation_id":"a5f6be9d-dc05-48b0-b164-1e9100217732","resolution":{"observed_at":"2026-08-14T15:13:40.716564Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-14T15:13:40.730512Z","title":"Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty","venue":null,"work_id":"104cda85-c66e-4922-959d-6605d20daeb0","year":2013},"citing_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"reference_index":2014,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:40.571565Z"},"links":{"citing_paper":"/paper/1908.01786"},"observation_digest":"sha256:3f69c7c8aea10a12fd0d693097ef0149cfabad18ac1ab2236762a4b15e166f80","observation_id":"aa49a977-1fa7-4a08-a25c-43e475c019cf","resolution":{"observed_at":"2026-08-14T15:13:40.736349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-14T15:13:40.634671Z","title":"5249–5255","venue":null,"work_id":"3a3b08c3-aa08-4d73-b9e0-fc8cc190a9ac","year":1969},"citing_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"reference_index":2017,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:40.597453Z"},"links":{"citing_paper":"/paper/1908.01786"},"observation_digest":"sha256:7c768937bb1d5f2ef13dedc6691052fb6a19e699e10ead375b3ba2bceaa3a80f","observation_id":"69d14d21-4c0d-49be-98a3-f953e538ef16","resolution":{"observed_at":"2026-08-14T15:13:40.640785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-14T15:13:40.765272Z","title":"1341–1348","venue":null,"work_id":"a492086b-fbba-4c4c-be3f-6183c2242a06","year":2005},"citing_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:40.558679Z"},"links":{"citing_paper":"/paper/1908.01786"},"observation_digest":"sha256:a38d549f7c96ae397207f78c7b84512cd7dc47cf4d862374319f807408f18136","observation_id":"70ee441b-3e78-497a-a7cd-ba4550198111","resolution":{"observed_at":"2026-08-14T15:13:40.771000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-14T15:13:40.798867Z","title":"4747–4754","venue":null,"work_id":"6a581ea1-cb65-4f1d-ac76-9bb6f71965bc","year":2017},"citing_paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes","version":2},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-14T15:13:40.544397Z"},"links":{"citing_paper":"/paper/1908.01786"},"observation_digest":"sha256:b48a460780301a1ee848722753fa2c1f1090ce43ad63940b3fbc16b86eeb57fa","observation_id":"50319675-7862-41ab-a0db-ec43c0f06c5f","resolution":{"observed_at":"2026-08-14T15:13:40.804693Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.01786","last_updated":"2020-05-24T10:40:37Z","latest_version":2,"primary_category":"math.OC","snapshot_observed_at":"2026-08-20T03:09:35.928728Z","submitted_at":"2019-08-05T18:06:23Z","title":"Stochastic data-driven model predictive control using Gaussian processes"},"reference_resolution":{"displayed":12,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":0,"verified_fuzzy":10},"total_outbound_references":12},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 2 inbound Pith citation observations for arXiv:1908.01786."}