{"as_of":"2026-08-14T06:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0002f74308997c3455c7c9204f224f269bedc552e5e9289f187782dde2f1b5ff","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":2,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":2,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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":"2026-08-12T11:30:37.867747Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-11T07:06:16.457014Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2407.01001","last_updated":"2024-07-01T06:31:41Z","snapshot_observed_at":"2026-08-13T20:10:32.190587Z","submitted_at":"2024-07-01T06:31:41Z","title":"Flood Prediction Using Classical and Quantum Machine Learning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.01001","snapshot_observed_at":"2026-08-12T11:30:37.867747Z","title":"arXiv preprint arXiv:2407.01001 (2024)","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.18141","last_updated":"2024-11-27T08:43:07Z","snapshot_observed_at":"2026-08-12T11:26:13.868878Z","submitted_at":"2024-11-27T08:43:07Z","title":"Predicting Water Quality using Quantum Machine Learning: The Case of the Umgeni Catchment (U20A) Study Region","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T11:30:37.867747Z"},"links":{"cited_paper":"/paper/2407.01001","citing_paper":"/paper/2411.18141"},"observation_digest":"sha256:7be73074074ee6aa284b7544ec9a85aa2db13da962ff1657bc36e5f1ba8a4cbc","observation_id":"0fd369a0-932d-454f-8b3e-4bfdbda60513","resolution":{"observed_at":"2026-08-12T11:30:37.867747Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.01001","last_updated":"2024-07-01T06:31:41Z","snapshot_observed_at":"2026-08-13T20:10:32.190587Z","submitted_at":"2024-07-01T06:31:41Z","title":"Flood Prediction Using Classical and Quantum Machine Learning Models","version":1},"cited_work":{"arxiv_id":"2407.01001","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2407.01001","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Flood prediction using classical and quantum machine learning models","venue":null,"work_id":"bdec767b-a2ed-456e-b4f8-e0a5cbd48fcd","year":2024},"citing_paper":{"arxiv_id":"2604.09374","last_updated":"2026-04-14T03:27:34Z","snapshot_observed_at":"2026-08-02T06:50:38.431644Z","submitted_at":"2026-04-10T14:45:38Z","title":"Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-10T17:17:15.552151Z"},"links":{"cited_paper":"/paper/2407.01001","citing_paper":"/paper/2604.09374"},"observation_digest":"sha256:cbf22a21f3d95523a78672d406eedcdb0fbb5166e88c9d94a35f2f040eb11ea5","observation_id":"a1365fe3-90ce-4084-95e6-bf02278d0726","resolution":{"observed_at":"2026-05-11T07:06:16.464938Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2407.01001/citation-record","integrity":"/paper/2407.01001/integrity","json":"/paper/2407.01001/citation-record.json","paper":"/paper/2407.01001"},"outbound":[],"paper":{"arxiv_id":"2407.01001","last_updated":"2024-07-01T06:31:41Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T20:10:32.190587Z","submitted_at":"2024-07-01T06:31:41Z","title":"Flood Prediction Using Classical and Quantum Machine Learning Models"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.01001."}