{"as_of":"2026-08-20T19:02:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0ad241dabcd96fa3285e91ddac325a44b0f081a892f3c5ae24d2c5131ecdb6b1","coverage":[{"denominator":22,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":22,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T10:34:20.096049Z","state":"measured"},{"denominator":22,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":22,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2504.18599/citation-record","integrity":"/paper/2504.18599/integrity","json":"/paper/2504.18599/citation-record.json","paper":"/paper/2504.18599"},"outbound":[{"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:19.807987Z","title":null,"venue":null,"work_id":null,"year":2019},"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":2,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.807987Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:a6f8e963727f5158a5f7046d0f5a7db5f1f8106128156aefd7cd1a52c413020b","observation_id":"fa780963-fb70-45df-88b0-8b28e092e2f2","resolution":{"observed_at":"2026-08-16T10:34:19.807987Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:19.821688Z","title":"P., & Al Faruque, M","venue":null,"work_id":null,"year":2020},"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":3,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.821688Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:20e1dd472639e8600fded57b029f09fb95f8671c3e090c73f3bf77d3f53781a7","observation_id":"f6d5b694-1e8e-4ab3-a12e-8cb054e6b523","resolution":{"observed_at":"2026-08-16T10:34:19.821688Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.camwa.2023.09.011","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:20.487212Z","title":"S., Yaseen, M., Rawat, S","venue":null,"work_id":"df1dc382-9685-465e-bfd7-60cc2019ac54","year":2024},"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":4,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.829903Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:3636dad21d8dd9545bfde3fa27a812fa68933831989219c68e8ac598e7e2f215","observation_id":"7497f745-144a-4914-b77a-0fdd1973907e","resolution":{"observed_at":"2026-08-16T10:34:20.504547Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:21.950467Z","title":"M., & Thomas, J","venue":null,"work_id":"3a7815a2-8279-4c13-9de0-5ee88f98a58c","year":2006},"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":5,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.839333Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:513c7c1ab46ec115d4ea316b88f5e896690b2c05d7b78ca07767a71f5dee0006","observation_id":"f73de6c3-8c2b-4035-8845-5cc84148a442","resolution":{"observed_at":"2026-08-16T10:34:21.965151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2003.81124","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:21.219629Z","title":null,"venue":null,"work_id":"9c4f3ef8-7304-4a7a-bd65-231e3fbc1ba1","year":2003},"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":6,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.847081Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:04c3b2337b3a1138d34537caa470f543571635e01dbe8279814d7e34b69fd66c","observation_id":"ea1833ff-e8a6-4e1f-beb3-dd18f81997e3","resolution":{"observed_at":"2026-08-16T10:34:21.243730Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:19.854936Z","title":"N., Costa, A","venue":null,"work_id":null,"year":2020},"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":7,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.854936Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:39cca72d1ec7f2fca30e978b49535762a734a689ca9db0d1dc1744c17f3f67f7","observation_id":"b7175580-f25a-4660-975b-45d1aa77ae23","resolution":{"observed_at":"2026-08-16T10:34:19.854936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:19.873554Z","title":null,"venue":null,"work_id":null,"year":2019},"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":8,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.873554Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:1712217d1331ed3ac56bc9889aca61d1d83462681673cc9dd318702834e32d34","observation_id":"0260201c-ecbb-48c7-8ea7-43a77f8d8dee","resolution":{"observed_at":"2026-08-16T10:34:19.873554Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:21.916335Z","title":null,"venue":null,"work_id":"f8d6871f-a0d4-4edb-b80c-b641264c1852","year":2005},"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":9,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.898965Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:0a55ec32b9aafdea63b7e601693fb57d82b46d486827b6beb07612af7c5ed55e","observation_id":"11093107-bef6-45e6-84d0-6b9bf5613fbc","resolution":{"observed_at":"2026-08-16T10:34:21.929727Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:21.874734Z","title":null,"venue":null,"work_id":"ef37d48f-4701-4d5f-a5cf-e6e794023aa7","year":2019},"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":10,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.913547Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:e79ee877e3ad4f1826225dfd7e7a476bbc5196b04bf6913aeee4ec128ba4d4e4","observation_id":"a449bac1-887d-415e-b223-a3388a25aedd","resolution":{"observed_at":"2026-08-16T10:34:21.891092Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.heliyon.2023.e16892","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:20.392872Z","title":"K., Yaseen, M., & Pant, M","venue":null,"work_id":"4cc1616d-5587-424a-8d93-c00fc279d874","year":2023},"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":11,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.941808Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:8b1b8eea46d94062f7edb01b5926cd5625601bb6cd32463ab384bbfe609e0227","observation_id":"363d8be4-1f6b-4063-a3e4-039351e35d35","resolution":{"observed_at":"2026-08-16T10:34:20.403334Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:19.949000Z","title":"W., & Padgett, W","venue":null,"work_id":null,"year":2011},"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":12,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.949000Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:180e8c9e72633e355899c18d93b15309ccdc3e93f6d49569246e6cde8b3869d7","observation_id":"2b64ea77-4111-44b1-8eb0-ae65f20c3b1e","resolution":{"observed_at":"2026-08-16T10:34:19.949000Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2023.10701","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:20.941240Z","title":"K., Yaseen, M., Pant, M., Ujarari, C","venue":null,"work_id":"37775226-dd24-4d92-9faa-9eb522e8e4dc","year":2023},"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":13,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.954865Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:02046de05d4a9e0138a483f4441522255ce8baff446cf634b133d02742044e19","observation_id":"8e21ef5a-f4a4-412d-9bb2-11b427947f34","resolution":{"observed_at":"2026-08-16T10:34:20.958966Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/0306-4549(95)00005-y","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:20.305429Z","title":null,"venue":null,"work_id":"a04f6c4e-80f1-43e7-9c0b-b30eee66881e","year":1995},"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":14,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.963856Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:933c0688dfd0dd90faccda8b50ab6a0e188dd2024fa0d756525e9dcaeb6c2601","observation_id":"13adc3eb-056f-45e9-8aee-e6c0483ca1e6","resolution":{"observed_at":"2026-08-16T10:34:20.327920Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:21.821881Z","title":null,"venue":null,"work_id":"d5c088f9-7bd0-4185-839e-8f88d070a9b5","year":2014},"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":15,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.970285Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:b862abcab65fbc06180a3b568aa940252781610ef4200b3448b832e1e207e651","observation_id":"aec3bc30-0d78-4589-bddf-8de99195c65a","resolution":{"observed_at":"2026-08-16T10:34:21.835138Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:21.785565Z","title":null,"venue":null,"work_id":"b84925ba-973b-44ab-8c8f-ebb4a7f96c19","year":2009},"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":16,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.979644Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:5e7a3b134744728e03988a45dabefc563d87b85ddff6b5e61b38d78ff1c3c8ae","observation_id":"c213ded1-5c3a-4da8-b14a-909542a10114","resolution":{"observed_at":"2026-08-16T10:34:21.798530Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:19.996965Z","title":null,"venue":null,"work_id":null,"year":2020},"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":17,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:19.996965Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:40ffe43cd4b1c80ca6dea7176b83592e4155bca1f8786b71c4d591d21eeb2bf8","observation_id":"d7be0177-a184-4be7-ad25-882a2c60f17d","resolution":{"observed_at":"2026-08-16T10:34:19.996965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:21.734657Z","title":null,"venue":null,"work_id":"d49bafe8-dd56-4fd0-9a6e-b71643c1aa1d","year":2015},"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":18,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:20.005408Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:111655601aad8b023044653284a1e88f43bba58b001cfe87c2e77fef4a735499","observation_id":"aff634bc-1568-458a-9164-602eec8053e2","resolution":{"observed_at":"2026-08-16T10:34:21.746331Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:20.026413Z","title":"(2015, July)","venue":null,"work_id":null,"year":2015},"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":19,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:20.026413Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:4b0678c76982100eecf2bd1e4f13fb71c22810c1059a7c7dde533c919ba171ba","observation_id":"93cd47d9-3ff9-45f4-be87-f38c3453a6d0","resolution":{"observed_at":"2026-08-16T10:34:20.026413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.neucom.2017.08.049","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:20.251772Z","title":null,"venue":null,"work_id":"cc976754-454d-4c60-87cd-34ce80602e08","year":2018},"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":20,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:20.048960Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:ceb635f7be69a49bcf427d47e703bdd30a6629ff92a0808b320a54a2f148d4d4","observation_id":"8df1bc5e-0ada-44f7-b821-096b4258da20","resolution":{"observed_at":"2026-08-16T10:34:20.274324Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/1361-6528/acf3a7","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:20.206020Z","title":"K., Khan, U., Sarris, I","venue":null,"work_id":"68ff0c18-a24e-4d12-aec6-36098c03f3d6","year":2023},"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":21,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:20.085574Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:7d688df9bb004092ac82475cafe40cf61d4f48894e2a9adb815603be09aa1215","observation_id":"fb0924d7-b2e9-47c3-ba84-5a64e5ce5a37","resolution":{"observed_at":"2026-08-16T10:34:20.225277Z","resolver_source":"doi","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"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T10:34:21.686061Z","title":null,"venue":null,"work_id":"95bbf16d-3695-4de6-87bd-500a97acb4a6","year":2018},"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":22,"source":"pdf_text","source_observed_at":"2026-08-16T10:34:20.096049Z"},"links":{"citing_paper":"/paper/2504.18599"},"observation_digest":"sha256:3261e85b4a39fa29dded40174f22f2a8d756d461fc219702b0c22cc17e7412ae","observation_id":"2947ebf4-7068-47f3-bb82-2fb8d512ece1","resolution":{"observed_at":"2026-08-16T10:34:21.697382Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.18599","last_updated":"2025-04-24T18:23:18Z","latest_version":1,"primary_category":"cs.LG","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"},"reference_resolution":{"displayed":22,"state_counts":{"malformed_identifier":1,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":13,"verified_exact":5,"verified_fuzzy":1},"total_outbound_references":22},"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 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2504.18599."}