{"as_of":"2026-08-20T14:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:803d3a6afe1a19cb3d3b0d409b0328d521c1869f0b7a653a20645edc94ca4869","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T22:46:06.407684Z","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":"2026-07-02T15:37:05.983817Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2410.13867","last_updated":"2024-10-02T08:25:57Z","snapshot_observed_at":"2026-08-16T13:13:28.239136Z","submitted_at":"2024-10-02T08:25:57Z","title":"Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performance","version":1},"cited_work":{"arxiv_id":"2410.13867","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.13867","snapshot_observed_at":"2026-07-02T15:37:05.983817Z","title":null,"venue":null,"work_id":"7e8314fc-d332-4e57-af0a-e9019c96859f","year":2024},"citing_paper":{"arxiv_id":"2605.27583","last_updated":"2026-05-26T18:52:57Z","snapshot_observed_at":"2026-08-12T17:16:54.947430Z","submitted_at":"2026-05-26T18:52:57Z","title":"Information-theoretic Multimodal Representation Learning for Electrocardiogram Signals","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-06-29T18:31:41.148797Z"},"links":{"cited_paper":"/paper/2410.13867","citing_paper":"/paper/2605.27583"},"observation_digest":"sha256:9efebda14374367d2ae0ecf26b7fcc89dbc911804111602d3bf570dd806240da","observation_id":"75497327-a808-43a7-a77d-1b60c37846cd","resolution":{"observed_at":"2026-06-29T18:33:50.343420Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2410.13867","last_updated":"2024-10-02T08:25:57Z","snapshot_observed_at":"2026-08-16T13:13:28.239136Z","submitted_at":"2024-10-02T08:25:57Z","title":"Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performance","version":1},"cited_work":{"arxiv_id":"2410.13867","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2410.13867","snapshot_observed_at":"2026-07-02T15:37:05.983817Z","title":null,"venue":null,"work_id":"7e8314fc-d332-4e57-af0a-e9019c96859f","year":2024},"citing_paper":{"arxiv_id":"2607.00958","last_updated":"2026-07-01T13:56:21Z","snapshot_observed_at":"2026-08-13T00:58:31.331046Z","submitted_at":"2026-07-01T13:56:21Z","title":"LeNEPA: No-Augmentation Next-Latent Prediction for Time-Series Representation Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-02T15:32:42.523362Z"},"links":{"cited_paper":"/paper/2410.13867","citing_paper":"/paper/2607.00958"},"observation_digest":"sha256:50ad631a509318ba80b66ee85d1b686b60463f1e81e19a40ac46cbaf23f112c8","observation_id":"362a8df8-9d8f-41dd-bb25-10dc90f1979d","resolution":{"observed_at":"2026-07-02T15:37:05.985129Z","resolver_source":"arxiv_id","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":{"arxiv_id":"2410.13867","last_updated":"2024-10-02T08:25:57Z","snapshot_observed_at":"2026-08-16T13:13:28.239136Z","submitted_at":"2024-10-02T08:25:57Z","title":"Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.13867","snapshot_observed_at":"2026-08-01T22:46:06.361165Z","title":"Self-Supervised Pre-Training with Joint-Embedding Pre- dictive Architecture Boosts ECG Classification Performance","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.16350","last_updated":"2026-07-17T05:28:22Z","snapshot_observed_at":"2026-08-14T03:36:12.798074Z","submitted_at":"2026-07-17T05:28:22Z","title":"Joint-Embedding Predictive Architecture for Sensor-based Activity Recognition","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T22:46:06.361165Z"},"links":{"cited_paper":"/paper/2410.13867","citing_paper":"/paper/2607.16350"},"observation_digest":"sha256:e8414fff84d2ede9164a68409b99ea1fa70868067e5784c29cf3cc7783919d88","observation_id":"044faf92-3263-42a3-8001-802681367ffe","resolution":{"observed_at":"2026-08-01T22:46:06.361165Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.13867","last_updated":"2024-10-02T08:25:57Z","snapshot_observed_at":"2026-08-16T13:13:28.239136Z","submitted_at":"2024-10-02T08:25:57Z","title":"Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.13867","snapshot_observed_at":"2026-08-01T22:46:06.407684Z","title":"URL: http://arxiv.org/abs/ 2410.13867 (visited on 01/22/2026)","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.16350","last_updated":"2026-07-17T05:28:22Z","snapshot_observed_at":"2026-08-14T03:36:12.798074Z","submitted_at":"2026-07-17T05:28:22Z","title":"Joint-Embedding Predictive Architecture for Sensor-based Activity Recognition","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-01T22:46:06.407684Z"},"links":{"cited_paper":"/paper/2410.13867","citing_paper":"/paper/2607.16350"},"observation_digest":"sha256:6d98bb963c07afb13500d63c52b61125c685c96107a3834b3ab313256d0989f2","observation_id":"729111b1-a90b-4742-9871-97627119e582","resolution":{"observed_at":"2026-08-01T22:46:06.407684Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2410.13867/citation-record","integrity":"/paper/2410.13867/integrity","json":"/paper/2410.13867/citation-record.json","paper":"/paper/2410.13867"},"outbound":[],"paper":{"arxiv_id":"2410.13867","last_updated":"2024-10-02T08:25:57Z","latest_version":1,"primary_category":"eess.SP","snapshot_observed_at":"2026-08-16T13:13:28.239136Z","submitted_at":"2024-10-02T08:25:57Z","title":"Self-Supervised Pre-Training with Joint-Embedding Predictive Architecture Boosts ECG Classification Performance"},"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 4 inbound Pith citation observations for arXiv:2410.13867."}