{"as_of":"2026-08-16T03:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ebd3aa360377ba92f4cacb8d0e93ee0e9e56bb19ab2d3e43de9b0b82d17584b8","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":"2026-08-15T06:32:42.880941+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":"2026-08-14T13:22:59.584343Z","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":"2026-08-14T05:30:56.915299Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1805.02410","last_updated":"2018-05-29T09:09:29Z","snapshot_observed_at":"2026-08-14T19:18:09.130398Z","submitted_at":"2018-05-07T09:18:25Z","title":"MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.02410","snapshot_observed_at":"2026-08-14T13:22:59.584343Z","title":"Takahashi, N","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.05182","last_updated":"2019-08-14T15:56:34Z","snapshot_observed_at":"2026-08-15T23:32:14.338416Z","submitted_at":"2019-08-14T15:56:34Z","title":"Interleaved Multitask Learning for Audio Source Separation with Independent Databases","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T13:22:59.584343Z"},"links":{"cited_paper":"/paper/1805.02410","citing_paper":"/paper/1908.05182"},"observation_digest":"sha256:165c8d59318eaa581c110da9e03279a9e753ada68cea0405b8172675f91ad5a0","observation_id":"b8c5e1d8-ce1a-4cdd-a0d7-72ef46359b42","resolution":{"observed_at":"2026-08-14T13:22:59.584343Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1805.02410","last_updated":"2018-05-29T09:09:29Z","snapshot_observed_at":"2026-08-14T19:18:09.130398Z","submitted_at":"2018-05-07T09:18:25Z","title":"MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation","version":2},"cited_work":{"arxiv_id":"1805.02410","doi":null,"metadata_source":"pith","pith_arxiv_id":"1805.02410","snapshot_observed_at":"2026-08-14T05:30:56.915299Z","title":"MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation","venue":"cs.SD","work_id":"0e376f4a-aa63-4354-83ee-e4acd893b00b","year":2018},"citing_paper":{"arxiv_id":"1909.01174","last_updated":"2019-09-03T13:41:56Z","snapshot_observed_at":"2026-08-15T22:53:28.311152Z","submitted_at":"2019-09-03T13:41:56Z","title":"Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T05:30:56.864041Z"},"links":{"cited_paper":"/paper/1805.02410","citing_paper":"/paper/1909.01174"},"observation_digest":"sha256:1162f16dfecb24584bf82828ab1e05e3ce5af9fc8dff82524e8ffd54a23571d4","observation_id":"094c910c-6aa7-4faf-b824-9c300a5fd38a","resolution":{"observed_at":"2026-08-14T05:30:56.920906Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1805.02410/citation-record","integrity":"/paper/1805.02410/integrity","json":"/paper/1805.02410/citation-record.json","paper":"/paper/1805.02410"},"outbound":[],"paper":{"arxiv_id":"1805.02410","last_updated":"2018-05-29T09:09:29Z","latest_version":2,"primary_category":"cs.SD","snapshot_observed_at":"2026-08-14T19:18:09.130398Z","submitted_at":"2018-05-07T09:18:25Z","title":"MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation"},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1805.02410."}