{"as_of":"2026-08-15T19:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1f247f167f555de6e5d3fbf5987a4fe69947559ef1a48ee21615ea9d489f9439","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":"2026-08-15T06:32:42.880941+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":"2026-07-10T18:44:34.040973Z","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":"2026-07-10T18:47:31.521739Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2103.09316","last_updated":"2022-03-19T05:59:11Z","snapshot_observed_at":"2026-08-13T19:58:33.504752Z","submitted_at":"2021-03-14T16:24:04Z","title":"Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison","version":3},"cited_work":{"arxiv_id":"2103.09316","doi":null,"metadata_source":"pith","pith_arxiv_id":"2103.09316","snapshot_observed_at":"2026-07-10T18:47:31.521739Z","title":"Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison","venue":"cs.LG","work_id":"3ccd1f74-1663-48aa-b58b-23bc766df461","year":2021},"citing_paper":{"arxiv_id":"2607.07767","last_updated":"2026-07-08T15:51:05Z","snapshot_observed_at":"2026-08-14T09:03:32.798030Z","submitted_at":"2026-07-08T15:51:05Z","title":"Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-10T18:44:34.040973Z"},"links":{"cited_paper":"/paper/2103.09316","citing_paper":"/paper/2607.07767"},"observation_digest":"sha256:7af7a866aefa4e726a6034e23e08b3de8fda55bba7257d0163f0ca0364eb6cef","observation_id":"6a60366e-9658-4431-866d-1efd6bf68ae8","resolution":{"observed_at":"2026-07-10T18:47:31.523489Z","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/2103.09316/citation-record","integrity":"/paper/2103.09316/integrity","json":"/paper/2103.09316/citation-record.json","paper":"/paper/2103.09316"},"outbound":[],"paper":{"arxiv_id":"2103.09316","last_updated":"2022-03-19T05:59:11Z","latest_version":3,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-13T19:58:33.504752Z","submitted_at":"2021-03-14T16:24:04Z","title":"Are deep learning models superior for missing data imputation in large surveys? Evidence from an empirical comparison"},"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 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 1 inbound Pith citation observation for arXiv:2103.09316."}