{"as_of":"2026-08-10T18:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7bf801348b0dd337aef03b5c046582d9fc46383328a94f99072a5a6676a9b712","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":"2026-08-10T06:31:04.303077+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":"2026-08-07T22:45:38.974210Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"pith","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":0,"observed_at":"2026-08-05T02:28:24.338817Z","source":"pith"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2101.12037","last_updated":"2021-01-28T14:54:01Z","snapshot_observed_at":"2026-08-07T01:19:25.940672Z","submitted_at":"2021-01-28T14:54:01Z","title":"BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2101.12037","snapshot_observed_at":"2026-08-07T22:45:38.974210Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.17470","last_updated":"2025-02-27T02:23:20Z","snapshot_observed_at":"2026-08-09T18:07:22.279661Z","submitted_at":"2025-02-13T08:33:38Z","title":"MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification","version":3},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T22:45:38.974210Z"},"links":{"cited_paper":"/paper/2101.12037","citing_paper":"/paper/2502.17470"},"observation_digest":"sha256:6116dbeb2ebfc72faab9d3beadf3994f5a5d9e54d8ec91c4dd6504a8e2c393f8","observation_id":"73f56067-558d-4379-9aaf-05158a3cb4db","resolution":{"observed_at":"2026-08-07T22:45:38.974210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2101.12037","last_updated":"2021-01-28T14:54:01Z","snapshot_observed_at":"2026-08-07T01:19:25.940672Z","submitted_at":"2021-01-28T14:54:01Z","title":"BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data","version":1},"cited_work":{"arxiv_id":"2101.12037","doi":"10.48550/arxiv.2101.12037","metadata_source":"pith","pith_arxiv_id":"2101.12037","snapshot_observed_at":"2026-08-05T02:49:54.815029Z","title":"BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data","venue":"cs.LG","work_id":"5dec8d81-fb8c-48dd-a1b5-9c035db489a2","year":2021},"citing_paper":{"arxiv_id":"2607.06629","last_updated":"2026-07-09T11:36:51Z","snapshot_observed_at":"2026-08-07T01:17:33.686416Z","submitted_at":"2026-07-07T12:19:54Z","title":"STST-JEPA: Shallow-Target Spatio-Temporal Joint Embedding Prediction Architecture For EEG Self-Supervised Learning","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-11T01:07:21.002787Z"},"links":{"cited_paper":"/paper/2101.12037","citing_paper":"/paper/2607.06629"},"observation_digest":"sha256:99b49e3ebf3da56d18f3b88e4e793190cb3cc4fced6fe34db45dfaaf14e19b32","observation_id":"e9780fed-ca2f-4979-b2e5-8dfde624d75e","resolution":{"observed_at":"2026-07-11T01:07:41.817689Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-07-11T01:49:40.593827+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-11T01:49:40.593827+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2101.12037/citation-record","integrity":"/paper/2101.12037/integrity","json":"/paper/2101.12037/citation-record.json","paper":"/paper/2101.12037"},"outbound":[],"paper":{"arxiv_id":"2101.12037","last_updated":"2021-01-28T14:54:01Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-07T01:19:25.940672Z","submitted_at":"2021-01-28T14:54:01Z","title":"BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data"},"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":"2026-08-10T06:31:04.303077+00:00","source":"crossref"},{"observed_at":"2026-08-10T06:30:57.382061+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2101.12037."}