{"as_of":"2026-08-20T14:13:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0f981eadf2e6f58773ebf116d958c6b1d5a8d9eaf0980932e98df2ceeec47122","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":"2026-08-20T06:33:59.587034+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":"2026-08-16T10:34:19.795847Z","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":"2026-08-15T17:22:09.182003Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1607.02480","last_updated":"2016-07-08T18:20:32Z","snapshot_observed_at":"2026-08-14T21:49:10.892556Z","submitted_at":"2016-07-08T18:20:32Z","title":"Real-Time Anomaly Detection for Streaming Analytics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.02480","snapshot_observed_at":"2026-08-16T10:34:19.795847Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2504.18599","last_updated":"2025-04-24T18:23:18Z","snapshot_observed_at":"2026-08-16T10:28:11.026128Z","submitted_at":"2025-04-24T18:23:18Z","title":"A Hybrid Framework for Real-Time Data Drift and Anomaly Identification Using Hierarchical Temporal Memory and Statistical Tests","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.795847Z"},"links":{"cited_paper":"/paper/1607.02480","citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:f1757220dc6f9e950577e1032400cb132a0f63d21439f6c7cfffba419f3194e1","observation_id":"a1aee0c5-a4e4-452e-a59a-529e0abff707","resolution":{"observed_at":"2026-08-16T10:34:19.795847Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1607.02480","last_updated":"2016-07-08T18:20:32Z","snapshot_observed_at":"2026-08-14T21:49:10.892556Z","submitted_at":"2016-07-08T18:20:32Z","title":"Real-Time Anomaly Detection for Streaming Analytics","version":1},"cited_work":{"arxiv_id":"1607.02480","doi":null,"metadata_source":"pith","pith_arxiv_id":"1607.02480","snapshot_observed_at":"2026-08-15T17:22:09.182003Z","title":"Real-Time Anomaly Detection for Streaming Analytics","venue":"cs.AI","work_id":"56bef555-149f-4ac4-a768-cdd8f1e3e020","year":2016},"citing_paper":{"arxiv_id":"2508.12643","last_updated":"2025-08-18T06:08:56Z","snapshot_observed_at":"2026-08-19T20:32:25.009680Z","submitted_at":"2025-08-18T06:08:56Z","title":"Learn Faster and Remember More: Balancing Exploration and Exploitation for Continual Test-time Adaptation","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-15T17:22:09.107551Z"},"links":{"cited_paper":"/paper/1607.02480","citing_paper":"/paper/2508.12643"},"observation_digest":"sha256:7ba932653fe6ebafa5b0492f2df325b3e8cf54efde2e3f6c896e73c48ff3cd8b","observation_id":"c603be8a-50e3-4dfd-906e-8a039993abbd","resolution":{"observed_at":"2026-08-15T17:22:09.187778Z","resolver_source":"local_arxiv","status":"verified_exact"},"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/1607.02480/citation-record","integrity":"/paper/1607.02480/integrity","json":"/paper/1607.02480/citation-record.json","paper":"/paper/1607.02480"},"outbound":[],"paper":{"arxiv_id":"1607.02480","last_updated":"2016-07-08T18:20:32Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-14T21:49:10.892556Z","submitted_at":"2016-07-08T18:20:32Z","title":"Real-Time Anomaly Detection for Streaming Analytics"},"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":"2026-08-20T06:33:59.587034+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 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1607.02480."}