{"as_of":"2026-08-03T21:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c18b385ea9f3e29b8c0c115650ca198a111acfa69623cd4756dec9ca78b79649","coverage":[{"denominator":53,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":53,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-22T12:43:18.499079Z","state":"measured"},{"denominator":53,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":53,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-03T06:30:56.289259+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/2509.26005/citation-record","integrity":"/paper/2509.26005/integrity","json":"/paper/2509.26005/citation-record.json","paper":"/paper/2509.26005"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Gaussian processes with linear operator inequality constraints","venue":null,"work_id":"7774f6c2-b66d-4670-bfeb-9673dc3f27c2","year":2019},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:eaab465a7bf79748168d0dece4c15d06ccbd5909a313758ccac5d6a066be11f8","observation_id":"5921d8a3-dcd9-4e3b-b17c-4ddbcf47b5c1","resolution":{"observed_at":"2026-05-22T12:44:52.836246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Kernels for vector-valued functions: A review","venue":null,"work_id":"7dc726c9-bb2e-41fe-ad7c-39be28487285","year":2012},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:1eebdb1c673acba86fb62fcf76175f68b99e70f235c8da979ce150cdc639b209","observation_id":"7b41fb0e-488d-4851-b2d5-be00c67feb04","resolution":{"observed_at":"2026-05-22T12:44:52.994827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"A Bayesian approach to Lagrangian data assimilation","venue":null,"work_id":"726b31f1-77ef-4caf-80fb-06a6d5c19b9f","year":2008},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:44dcd8ca05359d754c60034acc9e3f0f01131fa63d24082080b629b89911d7cc","observation_id":"20553035-a1a5-4166-995f-5d0064ec242c","resolution":{"observed_at":"2026-05-22T12:44:52.998084Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Gaussian processes at the Helm (holtz) a more fluid model for ocean currents","venue":null,"work_id":"76e12ca3-efe7-4b9d-b625-030e317a9311","year":2023},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:2b7ff0633c410349a4f17f26a2e42226712a2be3e4b5d691923d380dd89b9a45","observation_id":"55fee479-6622-439c-bc8a-a03da1820600","resolution":{"observed_at":"2026-05-22T12:44:52.990267Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"The Helmholtz-Hodge decomposition—a survey","venue":null,"work_id":"de4a753f-7842-4203-9e80-ddc4abcb6417","year":2012},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:a8f3e1f423402de94aeb372860b4882192b9890174b18e79a19d3ba98bb5a2fe","observation_id":"ce52307a-85bf-46ae-acfa-51a39e333ad2","resolution":{"observed_at":"2026-05-22T12:44:52.986752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"A causation-based computationally efficient strategy for deploying Lagrangian drifters to improve real-time state estimation","venue":null,"work_id":"ce6a21b9-6d16-4135-9e2c-e5ad60344ece","year":2024},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:8214eaa4bd743723f45803e8836af8fef9d033432d726b68e74ba8414a6a2134","observation_id":"95014442-9d19-41e3-848b-32a15593cd09","resolution":{"observed_at":"2026-05-22T12:44:52.983840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Lagrangian descriptors with uncertainty","venue":null,"work_id":"d41c0da4-5d21-4340-bdec-727f4bf663c3","year":2024},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:68183120125d538f1571254ce92d3971461c4eca7efe831b64334def46b604fc","observation_id":"d9313808-314d-4109-a12a-7d865dd2a6cd","resolution":{"observed_at":"2026-05-22T12:44:52.980639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Launching drifter observations in the presence of uncertainty","venue":null,"work_id":"7341e13e-3b05-47dc-9f03-16b5f8050cdb","year":2024},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:d7c7d28c33b86fcef7eb3e882ea0bbed08f2ed9c015e7f474cf10daec598ff43","observation_id":"5657cca3-8383-447c-92fd-c4558d9b1d3c","resolution":{"observed_at":"2026-05-22T12:44:52.977939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"18cfb59a-9d5b-420e-9d9f-b0ce3786face","year":2006},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:45c98edbf5c9cb95f6e10e326ce6cedf622f29f6561bdd8711b818e9cd6e9553","observation_id":"d9885737-88c9-48de-8907-a98279a441a2","resolution":{"observed_at":"2026-05-22T12:44:52.975147Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Ocean circulation kinetic energy: Reservoirs, sources, and sinks","venue":null,"work_id":"ef9c6992-3a46-4c40-9f4d-e348e202d117","year":2009},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:deedf4323433f6178e506788e52ce1f97e3dae2985170d8f0886188475c01848","observation_id":"19b1d588-01a0-41cb-91a0-79903f554270","resolution":{"observed_at":"2026-05-22T12:44:52.972330Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Deep adaptive design: Amortizing sequential Bayesian experimental design","venue":null,"work_id":"4a25e96f-d292-4eca-93c2-a7e758a05430","year":2021},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:8e262a4b6afece3786149cd0e3f67722303628c2eda9040cd0a07f383ce21427","observation_id":"a24b54c5-25e2-4246-b950-d89b122e8a00","resolution":{"observed_at":"2026-05-22T12:44:53.011995Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"An unstructured-grid, finite-volume, nonhydrostatic, parallel coastal ocean simulator","venue":null,"work_id":"721b55f2-6ddf-4524-af8e-895df7be8356","year":2006},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:70e086d39a4d84a3cc73729d2c84011be999aa42bdabbeb73d4031d3ed27383a","observation_id":"4ef21d0c-c5c8-46d2-adaf-584bd3828b2a","resolution":{"observed_at":"2026-05-22T12:44:52.969334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Surrogates: Gaussian Process Modeling, Design, and Optimization for the Applied Sciences","venue":null,"work_id":"9ead9a8c-fec5-4579-b877-81d3e315e519","year":2020},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:33ba46d6e3eea12f985af81bf7e2ae221cefe8aeee4740a2031b3284fbe948a8","observation_id":"52fb66df-9038-45c2-8811-285b16567668","resolution":{"observed_at":"2026-05-22T12:44:52.966082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Lagrangian Analysis and Prediction of Coastal and Ocean Dynamics","venue":null,"work_id":"b61e33ed-c302-4c89-a1c4-a72cfaf4be17","year":2007},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:4f7a001583f8ef79d336c5831c85fd8ae5e16f59e5e8a3dac9e226556b5a347b","observation_id":"d54f2e04-a67c-4ba4-8104-2b4d01d621f2","resolution":{"observed_at":"2026-05-22T12:44:52.963061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Spatio-temporal variational Gaussian processes","venue":null,"work_id":"23d716d4-2050-47cc-b33d-099dd0315a0f","year":2021},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:1fcff757651eeb16e4545e62d7b3868e4be67cac2e262b7fe4c1944826797b5e","observation_id":"98affb46-0fd5-4086-b079-bec65eb19e5e","resolution":{"observed_at":"2026-05-22T12:44:53.001259Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Physics-Informed Variational State-Space Gaussian Processes","venue":null,"work_id":"42de5761-fc8a-4e7a-b270-5cd015f66d5a","year":2024},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:a730a424d2c6d24ce0e3d314960a3229b6b98c01774e994d0ad520512c06f33f","observation_id":"53aafdb6-083e-4bbf-b066-5005a695e33d","resolution":{"observed_at":"2026-05-22T12:44:52.959184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Kalman filtering and smoothing solutions to temporal Gaussian process regression models","venue":null,"work_id":"3ec904b5-3f5f-4556-96d5-7925b8dc23e5","year":2010},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:200e09a28ad8336346bd54bfb21383e76fe85d5402b5f311ab0d1ac0c523b891","observation_id":"1371aa5d-1220-475e-bcb6-778e51b2af7c","resolution":{"observed_at":"2026-05-22T12:44:52.955814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Nesting particle filters for experimental design in dynamical systems","venue":null,"work_id":"d55ff0b0-bb1b-450b-96a5-e720213d1de9","year":2024},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:34f16ea54fa2ea5282c4f74a72a7b70f21de6ebdda1bd82bb1204b01535256aa","observation_id":"f2cd126d-7124-4507-a2e6-48ece58f9a18","resolution":{"observed_at":"2026-05-22T12:44:52.952736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Oil spill modeling: A critical review on current trends, perspectives, and challenges","venue":null,"work_id":"569f6470-f0ca-467a-8efb-5c02bb6fb78d","year":2021},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:75b87ef2baf93ab2391fe458374c9fdf0b0d23f36667d1d47c318e6b41c75460","observation_id":"aa068aad-7f21-4597-b1e9-b7f455bfc62a","resolution":{"observed_at":"2026-05-22T12:44:53.004636Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.02450","last_updated":"2025-05-28T14:14:29Z","snapshot_observed_at":"2026-07-06T20:31:03.484385Z","submitted_at":"2025-02-04T16:16:01Z","title":"Robust and Conjugate Spatio-Temporal Gaussian Processes","version":2},"cited_work":{"arxiv_id":"2502.02450","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2502.02450","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Robust and Conjugate Spatio-Temporal Gaussian Processes","venue":null,"work_id":"d88d40bd-8249-44e7-8649-f10bcc346fb8","year":2025},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"cited_paper":"/paper/2502.02450","citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:102506c5610656b81b0abaa149dd5060a4774ab8bfa89b1e6e98c59e9a6fec5b","observation_id":"f9fe8c2b-4090-4542-8a97-c836dbf5aa6b","resolution":{"observed_at":"2026-05-22T12:44:51.868580Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"A gridded surface current product for the Gulf of Mexico from consolidated drifter measurements","venue":null,"work_id":"1aa2be9a-00b4-4886-a62d-6012c42da26a","year":2021},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:3ca953f3400dda6dc08b7530b5128b9f2cf7f1801752027bbe1b598291d69164","observation_id":"fb5dc7c0-bee9-4df6-b9c5-2509762142a3","resolution":{"observed_at":"2026-05-22T12:44:52.949610Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Fractional Brownian motion, the Mat \\'e rn process, and stochastic modeling of turbulent dispersion","venue":null,"work_id":"dd12e466-c690-4578-b9a0-4e7d2f0af390","year":2017},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:b86f73ff83a117d05e86ae52f1311168352d7d80a2b133466e0c6fb83dac788f","observation_id":"434a6a1c-376b-4c6d-aebd-0b1bc8abe325","resolution":{"observed_at":"2026-05-22T12:44:52.946246Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach","venue":null,"work_id":"2f0f55d4-2a11-4346-8545-8a2dee4bde34","year":2011},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:ac5a4efce07c73083ae4ccaf2eb36b09a8b8331eeaa7f48820819523a15b9bdc","observation_id":"69b34bbf-f138-4255-86ba-b0bf67347475","resolution":{"observed_at":"2026-05-22T12:44:52.943011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"The SPDE approach for Gaussian and non-Gaussian fields: 10 years and still running","venue":null,"work_id":"8e517b11-7433-43d3-83bc-6b6ab8a24200","year":2022},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:2f1566ce14e279ba943b84a04ad0a1eba3d529802e4a62327b95dbb0050e9d39","observation_id":"d0de3266-00ab-4d8c-a0fe-fef44d20d3a0","resolution":{"observed_at":"2026-05-22T12:44:52.939604Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"On a measure of the information provided by an experiment","venue":null,"work_id":"a2a9ba5c-9eb4-4d2f-bdf1-5ca4f0dfc66c","year":1956},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:2ba00dea792651412d75d4ee1fa10229fd7c2c7ae1db145216747004241bceb8","observation_id":"b33d9893-cc58-41cd-9a1e-6f4f9088d6d4","resolution":{"observed_at":"2026-05-22T12:44:52.936668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Advances in the application of surface drifters","venue":null,"work_id":"71eb9a8c-6143-4639-a6d7-85a97c2d26a3","year":2017},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:6f3eb5b39332a7786a2b24b991f3dc5d9966666b63afe9b8935a3268b05b9dc1","observation_id":"1486e3cd-40c5-4dc7-8f7a-14a75934db49","resolution":{"observed_at":"2026-05-22T12:44:52.933988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Lagrangian descriptors: A method for revealing phase space structures of general time dependent dynamical systems","venue":null,"work_id":"596a0f6b-b118-4430-89e6-0834f3fecc5f","year":2013},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:88e1ed7e211efaabca56b2bec6fba77b40a1e518e7e62298391e4275efcac4c0","observation_id":"2bf8df8a-cd23-4c68-a423-7f43e50ee518","resolution":{"observed_at":"2026-05-22T12:44:53.008965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.21105/joss.04455","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-28T06:51:44.468569Z","title":"GPJax: A Gaussian Process Framework in JAX","venue":null,"work_id":"f41ca44e-189a-4ad1-8257-037408811acd","year":2022},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:36c69071eea85fecc880d92b96e6affba626c308db06b8164689f5387749e0cc","observation_id":"8907b1df-c9f0-4b08-bf1b-517fe2ea3e99","resolution":{"observed_at":"2026-05-22T12:44:51.801155Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Drifter launch strategies based on Lagrangian templates","venue":null,"work_id":"65e03691-ef2d-45c3-875b-da1f96d43298","year":2002},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:6cdef2416c876a05be6f65b5bfd5c77e4dfcaee9da928870af46f3ff537f9eab","observation_id":"7a639294-b9f5-4cb7-bc51-973cdf2ae495","resolution":{"observed_at":"2026-05-22T12:44:52.931054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Inferring flow energy, space scales, and timescales: freely drifting vs","venue":null,"work_id":"883e48eb-ef13-4835-98d6-61de9e506481","year":2024},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:7197309c0dc82cd73da8d223388d325261ca30eefefdfc15a87be6ddd0779010","observation_id":"ceb81a65-d34e-4578-8624-8429d896773c","resolution":{"observed_at":"2026-05-22T12:44:52.927869Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Modern Bayesian experimental design","venue":null,"work_id":"00864dca-d7ec-4f2f-860b-5cc3430abf90","year":2024},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:d4b451a2a3e4711c102d0f7e7764ae8d9271399e130770d797d698a9a9147ab5","observation_id":"3c5fbb2f-379d-459c-848e-6cd9e4c6bdce","resolution":{"observed_at":"2026-05-22T12:44:52.923260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"A seasonal harmonic model for internal tide amplitude prediction","venue":null,"work_id":"78a3ab6f-51ed-40c0-b0d1-b099f3bb8cc0","year":2021},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:60a223977e5e81093adc5443e2b54b8485d7b8e23ad51f70db197660bf00889d","observation_id":"ef65bb99-89be-400a-9c6f-13dfc5f4b3f6","resolution":{"observed_at":"2026-05-22T12:44:52.920613Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Monte Carlo Statistical Methods , volume 2","venue":null,"work_id":"ca4794d5-5cb3-4402-8130-a2a455538fb9","year":1999},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:7320739d342ea89b7fd64b81983ae792e7432911a94728bc24252b861aae4472","observation_id":"25b0a6c3-6e9e-47ee-89d7-61b457a0a982","resolution":{"observed_at":"2026-05-22T12:44:52.917277Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Gaussian Markov Random Fields: Theory and Applications","venue":null,"work_id":"de84f383-89e6-4d89-b1e4-412f396e3f1c","year":2005},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:ddbb92330ddfd24ae6e361c92f2cc3c4c3ab4562bb225d3886c66a0042fb3f40","observation_id":"0862bc25-ba25-4336-ba18-d7d23138252e","resolution":{"observed_at":"2026-05-22T12:44:52.908154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"A review of modern computational algorithms for Bayesian optimal design","venue":null,"work_id":"03512f31-e761-454b-8b7e-9ad30fba5c1b","year":2016},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:3e88c26dccae0b0259c53de76f34414570098b25fbe9f35cf4c937b395ee2ef3","observation_id":"a04d812b-4f54-4a4f-9c67-04b018d8c565","resolution":{"observed_at":"2026-05-22T12:44:52.849153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Scalable inference for structured Gaussian process models","venue":null,"work_id":"d27b1d1a-8b4e-4e96-bdd1-5d3d89b05b29","year":2012},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:fede5ba518ce5f89dc3bb68d093de267ce333ed11ade7d50d564f539d227c001","observation_id":"79e59a05-713e-45a6-8a9c-2457d328f957","resolution":{"observed_at":"2026-05-22T12:44:52.904743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Using flow geometry for drifter deployment in Lagrangian data assimilation","venue":null,"work_id":"9de1924e-74ef-44be-ac31-69950eecdc35","year":2008},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:4ff83403f17819c0f38b7e0f561b7e74d5b0f0b0907e1752a204be1555d9cd59","observation_id":"9a95917b-1e75-409a-8366-0b1c215342a5","resolution":{"observed_at":"2026-05-22T12:44:52.901561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Applied Stochastic Differential Equations , volume 10","venue":null,"work_id":"965fd6ca-ac61-4d8b-9bc6-a8e42b06d7d7","year":2019},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:567449ae2ee5b4f68847409b0fd7077d191f1e7e936e46446b97f33ff5d7f8a9","observation_id":"ebbe38cb-61e1-4044-acb8-e32ec72f38b6","resolution":{"observed_at":"2026-05-22T12:44:52.898036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Spatiotemporal learning via infinite-dimensional Bayesian filtering and smoothing: A look at Gaussian process regression through Kalman filtering","venue":null,"work_id":"855d6960-3c2a-424d-8da5-dd121f18d37c","year":2013},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:6cf76834d0c79bee47888e6dccaba9da78ca9727544e122ad84d06683c174d2c","observation_id":"37dbc13e-13ba-48f7-84ef-41a0d0e2e8a5","resolution":{"observed_at":"2026-05-22T12:44:52.893273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Active learning literature survey","venue":null,"work_id":"feeb7ff9-c480-45a2-856d-55b29e002002","year":2009},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:2adb837e0840372565b47735b1a42ab8cd7349bb8f611d1702606f48fc490d21","observation_id":"fa536cff-0448-42db-bbc4-b8c6b4188b90","resolution":{"observed_at":"2026-05-22T12:44:52.889811Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Prediction-Centric Uncertainty Quantification via MMD","venue":null,"work_id":"9d61927f-c672-4b31-8f78-8e4e169f034f","year":2025},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:7520ac9e76de678c26210e3f87c90f60722f57ca99a17c674fe12daf481da770","observation_id":"3a9077dd-4dac-4fc3-9c0b-6d65c2a10e5d","resolution":{"observed_at":"2026-05-22T12:44:52.886570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Stochastic differential equation methods for spatio-temporal Gaussian process regression","venue":null,"work_id":"8f499bc9-ab0b-4c43-8c74-6e730a3b389e","year":2016},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:281710cdbfd065a60435a0320cc34f76e8059e4ae2eda9fb0104dd2f08ba7569","observation_id":"360ab29e-8c6c-44af-a040-a4959c261e9b","resolution":{"observed_at":"2026-05-22T12:44:52.882656Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design","venue":null,"work_id":"a2029b07-1ff7-40b0-9690-0bd0e59d1209","year":2010},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:e0bc47599cb00a8464ffefeb7d981978ac9ac6ced23948dd8a8e6b518cf49eae","observation_id":"b69b1d89-0fc1-4561-aa5f-7284c5fe3cb0","resolution":{"observed_at":"2026-05-22T12:44:52.839601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"An Introduction to Numerical Analysis","venue":null,"work_id":"bb807a6d-d6a8-47e5-a27f-fe19c958845d","year":2003},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:be8a264f186d09d5d4fd692c94cbf38bf0ab393b5e4d3d988028c2f48f9fde04","observation_id":"2d53baa1-9cfe-4d68-b9d4-63de5b0c3990","resolution":{"observed_at":"2026-05-22T12:44:52.879125Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Numerical Linear Algebra , volume 50","venue":null,"work_id":"872c81e5-1926-4c00-97f2-32d5dd870068","year":1997},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:983b9abb086bf9d79741a542ddd5ccd6c67334412c9bf9bb9484a9e3b3f387ef","observation_id":"ee08c1a2-dc40-4839-b95f-8a665706146b","resolution":{"observed_at":"2026-05-22T12:44:52.875985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"An efficient drifters deployment strategy to evaluate water current velocity fields","venue":null,"work_id":"2fd821be-e442-4a9b-8315-68c013a374ec","year":2024},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:a7133076f4349c0a68290426b26692e03fd422f64956d1b8d8f02adb008de913","observation_id":"93800895-1865-43c1-87da-5d3aaa34a612","resolution":{"observed_at":"2026-05-22T12:44:52.872379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Dispersion of surface drifters in the tropical Atlantic","venue":null,"work_id":"3698b959-42e7-41c8-a5b2-440b650edf7f","year":2021},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:4648999c7894f559e4070a6fc90b6eb82bbd2febfcc47195488a704daff91b68","observation_id":"03ec158e-e34c-4b85-b737-fef98f112f1a","resolution":{"observed_at":"2026-05-22T12:44:52.845698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Nearshore internal bores and turbulent mixing in southern Monterey Bay","venue":null,"work_id":"5a7eb992-568c-4ed2-a088-72cf8a7846b7","year":2012},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:7093e25f4d5e7361ab204941c039c44af813a128f946200245b0b714a7fb547c","observation_id":"f0eaf690-95bf-4de8-b0ab-952f661c6387","resolution":{"observed_at":"2026-05-22T12:44:52.874165Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"On stationary processes in the plane","venue":null,"work_id":"2f522906-48aa-46cb-8d64-2d15001e6a5d","year":1954},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:0a80ff0acb9e73a84fc1892dca99867efb9f25531c4203b26a883a1a48f81552","observation_id":"adb53b6f-c8d1-4cd4-8d5c-78626a216304","resolution":{"observed_at":"2026-05-22T12:44:52.864790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Stochastic processes in several dimensions","venue":null,"work_id":"c6454187-3093-4f65-9046-4964864a27d2","year":1963},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:4da60c9fdf1ea74cf5ace1d7caf0ec34c66d054a9774c64a14bc18555607920f","observation_id":"2098f280-6c3f-4287-91e3-7d71969c8229","resolution":{"observed_at":"2026-05-22T12:44:52.860468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"Gaussian Processes for Machine Learning , volume 2","venue":null,"work_id":"c46bca81-c2a9-4f7a-a5ab-0b55695611e2","year":2006},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:2ac2bad4b53979483510d2c8a3061b672bd5914a923669c40a02f7b25b793dc7","observation_id":"d2602f0e-a9c4-4b8a-8cb9-90841271f7e4","resolution":{"observed_at":"2026-05-22T12:44:52.842796Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"HHD-GP: Incorporating Helmholtz-Hodge Decomposition into Gaussian Processes for Learning Dynamical Systems","venue":null,"work_id":"930ede49-4ab6-46e0-b09b-f15f9588c545","year":2024},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:3478140a8337ba1342c972169fe7532d39478e242d4597de974f67a1c957f922","observation_id":"ca9d89ea-2f4a-4363-aafe-8ec286729931","resolution":{"observed_at":"2026-05-22T12:44:52.856461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+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":"2026-06-05T21:23:00.469572Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories","venue":null,"work_id":"e146cd17-952a-4c16-b2ad-e84126c057de","year":2024},"citing_paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields","version":4},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-05-22T12:43:18.499079Z"},"links":{"citing_paper":"/paper/2509.26005"},"observation_digest":"sha256:cc3fa7fdd92eaad4ac04165c7b65921b7f2c3d2dc398fc0a712e4bfa307754e2","observation_id":"1f178c85-2fec-407d-8fcb-edb39499c7c1","resolution":{"observed_at":"2026-05-22T12:44:52.852859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.26005","last_updated":"2026-05-21T10:05:12Z","latest_version":4,"primary_category":"stat.ML","snapshot_observed_at":"2026-08-02T10:57:11.425100Z","submitted_at":"2025-09-30T09:36:57Z","title":"BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields"},"reference_resolution":{"displayed":53,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":2,"verified_fuzzy":50},"total_outbound_references":53},"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":"2026-08-03T06:30:56.289259+00:00","source":"crossref"},{"observed_at":"2026-08-03T06:30:50.922721+00:00","source":"retraction_watch"}],"thesis":"As of 3 August 2026, this Paper Citation Record lists 53 of 53 outbound references and 0 inbound Pith citation observations for arXiv:2509.26005."}