{"as_of":"2026-08-09T16:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f6a89235d6c9732a0fa1569d08b9d0ab75256214ae5eb5965160306698c79588","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":1,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":1,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T13:57:36.193917Z","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":"2026-08-05T13:57:37.804308Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1611.06694","last_updated":"2016-11-21T09:24:24Z","snapshot_observed_at":"2026-08-09T10:26:46.602676Z","submitted_at":"2016-11-21T09:24:24Z","title":"Training Sparse Neural Networks","version":1},"cited_work":{"arxiv_id":"1611.06694","doi":null,"metadata_source":"pith","pith_arxiv_id":"1611.06694","snapshot_observed_at":"2026-08-05T13:57:37.804308Z","title":"Training Sparse Neural Networks","venue":"cs.CV","work_id":"230801f1-3585-427c-964b-bb3b8b6a04b9","year":2016},"citing_paper":{"arxiv_id":"2509.00174","last_updated":"2025-09-13T17:01:49Z","snapshot_observed_at":"2026-08-08T08:42:35.761087Z","submitted_at":"2025-08-29T18:17:48Z","title":"Principled Approximation Methods for Efficient and Scalable Deep Learning","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T13:57:36.193917Z"},"links":{"cited_paper":"/paper/1611.06694","citing_paper":"/paper/2509.00174"},"observation_digest":"sha256:b1546da6733af224e28d9ea8801ca674db7dacacc8b1650ececab63bd97dab5c","observation_id":"11dcd3d5-9971-466d-acac-e083c272c802","resolution":{"observed_at":"2026-08-05T13:57:37.943742Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1611.06694/citation-record","integrity":"/paper/1611.06694/integrity","json":"/paper/1611.06694/citation-record.json","paper":"/paper/1611.06694"},"outbound":[],"paper":{"arxiv_id":"1611.06694","last_updated":"2016-11-21T09:24:24Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T10:26:46.602676Z","submitted_at":"2016-11-21T09:24:24Z","title":"Training Sparse Neural Networks"},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:1611.06694."}