{"as_of":"2026-08-21T15:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ad6fd8f25e5be17dbf3b30062b4163b057739184159da1ee5ede755cfd7134c0","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T16:35:28.051015Z","state":"measured"},{"denominator":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-21T06:32:19.484+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/2509.03290/citation-record","integrity":"/paper/2509.03290/integrity","json":"/paper/2509.03290/citation-record.json","paper":"/paper/2509.03290"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T16:35:28.203800Z","title":"A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems","venue":null,"work_id":"b7585f3f-daf0-4c52-b1ab-96b9063dacfc","year":2020},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.008808Z"},"links":{"citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:c1a29009902b68cbcb896f1479f6b4f3e135c4ccc11abc99c51684e0b214bed1","observation_id":"f9b1c073-a520-4c6b-a514-7e8c8704ceab","resolution":{"observed_at":"2026-08-15T16:35:28.207374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T16:35:28.193633Z","title":"Empowering the 6G Cellular Architecture With Open RAN","venue":null,"work_id":"2395057c-532c-49a2-982e-407e51947250","year":2024},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.012792Z"},"links":{"citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:64889cb2ca988eec6367e28c290787ad38e2ef443eba30a09725be71df43f1f0","observation_id":"4d9a5b91-11aa-4687-ba5b-298f8f10fb81","resolution":{"observed_at":"2026-08-15T16:35:28.197535Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T16:35:28.183374Z","title":"Anomaly detection in mobile networks","venue":null,"work_id":"4b40fb4a-3160-47ad-9b78-edac1151a2fd","year":2020},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.016535Z"},"links":{"citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:0d6166e19308b593da3f5c2a41cce549da8015158bc571c3b2e449bd4987ea78","observation_id":"0c05b40a-c15f-4e4e-8324-ca41d0972cfd","resolution":{"observed_at":"2026-08-15T16:35:28.186937Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T16:35:28.173188Z","title":"Anomaly detection and root cause analysis enabled by artificial intelligence","venue":null,"work_id":"20367538-6176-4208-892c-08e6b5f7d176","year":2020},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.020082Z"},"links":{"citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:a3f182b2d2799089cdbde863919f00739adf7d8aea4d71f48fde8a3375fae060","observation_id":"6f1c8f9c-dd25-4e87-b235-614cc73a63af","resolution":{"observed_at":"2026-08-15T16:35:28.176917Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T16:35:28.162736Z","title":"Uncovering latency anomalies in 5G RAN - A combination learner approach","venue":null,"work_id":"79b9c1ff-0c40-40cb-a837-c4a05e7eee58","year":2022},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.023513Z"},"links":{"citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:8ed6df2c646b81c7981b6e218b3695e1b175d41fb7a1901c70ec23ab22384f81","observation_id":"bebe5d50-ef4c-4f35-bd94-532fc33dedf2","resolution":{"observed_at":"2026-08-15T16:35:28.166444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T16:35:28.152289Z","title":"Benchmarking of anomaly detection techniques in O-RAN for handover optimization","venue":null,"work_id":"0613923e-5b66-4954-9eea-ce6bbedb0a78","year":2023},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.027136Z"},"links":{"citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:ef1b91d87554c8c16b089addcf1c87e65d0f3e2a1ec76517e11544bb358974c7","observation_id":"46de0a56-5d36-4f7a-ad68-8684403969ec","resolution":{"observed_at":"2026-08-15T16:35:28.156076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T16:35:28.141417Z","title":"SpotLight: Accurate, explainable and efficient anomaly detection for Open RAN","venue":null,"work_id":"d577e63f-fac2-43d3-8e3a-1e4f30112503","year":2024},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.030717Z"},"links":{"citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:9ce0330a0157b28927246ef3dc9fc600f7ba5ffa0c2c24ee62d7297f6acaff0e","observation_id":"55566096-6895-42ea-a4b9-5828fc05f3ec","resolution":{"observed_at":"2026-08-15T16:35:28.145542Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T16:35:28.130111Z","title":"Lundberg and et al","venue":null,"work_id":"1fac8fb2-c63e-4a4e-95b0-4dfbd372e358","year":2017},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.034156Z"},"links":{"citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:1b845a20e4c7757d9a879dc5ebe37e652250a68f3cf0cef70b4bc75fd58e467f","observation_id":"752afac6-ede1-4c48-8e6e-ca27fea9193d","resolution":{"observed_at":"2026-08-15T16:35:28.133578Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T16:35:28.120153Z","title":"Near Real-Time RAN Intelligent Controller E2 Service Model KPM","venue":null,"work_id":"a1597cea-28cf-44f0-b719-39df0624eaf3","year":2024},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.037704Z"},"links":{"citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:b3ba15f104869c770a48506808536485527dfb00d40a5cfd5cd6a16b0fcee1bc","observation_id":"9faf4008-1219-4aae-a7ca-ba57b997e493","resolution":{"observed_at":"2026-08-15T16:35:28.123573Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T16:35:28.109510Z","title":"Towards autonomous open radio access networks","venue":null,"work_id":"5eba497b-4c74-4705-845e-debda05df79e","year":2023},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.040928Z"},"links":{"citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:3abfd7fe161b384abb669ed43ed1e06be4be612a87a9a78c6f1d1436d15d0f97","observation_id":"2c9310ca-03bd-4d30-b679-e3a43adeabe6","resolution":{"observed_at":"2026-08-15T16:35:28.113188Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T16:35:28.097977Z","title":"Scalable and interpretable one-class svms with deep learning and random fourier features","venue":null,"work_id":"1a70342c-16fc-41a6-b844-7508ad074c71","year":2018},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.044228Z"},"links":{"citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:13e275373c0596b0e79a75e3519c622e2e8f7a89730865924e5a9f07e8ca6f65","observation_id":"53cf6406-620c-4a85-9809-8e0d76445153","resolution":{"observed_at":"2026-08-15T16:35:28.102405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+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":"2026-08-15T16:35:28.086123Z","title":"O-RAN-SC GitHub Page","venue":null,"work_id":"00b7fd59-f43d-4ca6-bb04-7915b13ed9fd","year":2021},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.047710Z"},"links":{"citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:67063f8dc33a329c3e7a7c89f8e3ea9921e0032058a52b8b8bf732f676f3af1d","observation_id":"009a948b-e7a6-43b7-98c6-5d199fa45fc1","resolution":{"observed_at":"2026-08-15T16:35:28.090708Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.12154","last_updated":"2024-12-11T07:53:20Z","snapshot_observed_at":"2026-08-15T01:58:02.043667Z","submitted_at":"2024-12-11T07:53:20Z","title":"PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.12154","snapshot_observed_at":"2026-08-15T16:35:28.051015Z","title":"PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T16:35:28.051015Z"},"links":{"cited_paper":"/paper/2412.12154","citing_paper":"/paper/2509.03290"},"observation_digest":"sha256:3b2ac8e3cc5e3315f67210dc86a7facfe1fdbd2c4f03915f6dc1d88357b97a35","observation_id":"670c335a-13c3-4236-948d-be065c62b46e","resolution":{"observed_at":"2026-08-15T16:35:28.051015Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.03290","last_updated":"2025-09-03T13:18:41Z","latest_version":1,"primary_category":"cs.NI","snapshot_observed_at":"2026-08-15T16:29:14.892231Z","submitted_at":"2025-09-03T13:18:41Z","title":"Machine Learning-Driven Anomaly Detection for 5G O-RAN Performance Metrics"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":0,"verified_fuzzy":12},"total_outbound_references":13},"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":"2026-08-21T06:32:19.484+00:00","source":"crossref"},{"observed_at":"2026-08-21T06:32:16.066871+00:00","source":"retraction_watch"}],"thesis":"As of 21 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2509.03290."}