{"as_of":"2026-08-17T07:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:91b54d235b1b757e65755a98f844f351af0410806a0ad7d322304939fdf6e517","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":3,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":3,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+00:00","state":"measured"},{"denominator":3,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":3,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T11:17:35.387925Z","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":"2026-05-25T05:45:24.044035Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2203.13746","last_updated":"2022-03-30T14:45:58Z","snapshot_observed_at":"2026-08-16T20:48:04.739588Z","submitted_at":"2022-03-25T16:23:02Z","title":"Code Smells for Machine Learning Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.13746","snapshot_observed_at":"2026-08-16T11:17:35.387925Z","title":"Code smells for machine learning applications,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.15989","last_updated":"2025-05-29T18:29:37Z","snapshot_observed_at":"2026-08-16T11:10:56.816544Z","submitted_at":"2025-04-22T15:51:00Z","title":"Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T11:17:35.387925Z"},"links":{"cited_paper":"/paper/2203.13746","citing_paper":"/paper/2504.15989"},"observation_digest":"sha256:4885473384c9a21ea8703ccfae7327c198b9a2269338bbd16fd8abc428f22777","observation_id":"c8097a88-ee63-4d89-8550-bb0f791874c5","resolution":{"observed_at":"2026-08-16T11:17:35.387925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.13746","last_updated":"2022-03-30T14:45:58Z","snapshot_observed_at":"2026-08-16T20:48:04.739588Z","submitted_at":"2022-03-25T16:23:02Z","title":"Code Smells for Machine Learning Applications","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.13746","snapshot_observed_at":"2026-08-03T15:10:20.696979Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2512.18020","last_updated":"2025-12-19T19:24:56Z","snapshot_observed_at":"2026-08-16T14:27:54.746428Z","submitted_at":"2025-12-19T19:24:56Z","title":"Specification and Detection of LLM Code Smells","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-03T15:10:20.696979Z"},"links":{"cited_paper":"/paper/2203.13746","citing_paper":"/paper/2512.18020"},"observation_digest":"sha256:e258f7807df3feb9336eac68878c32d54e513f8a5f98d6fb7c398bad4b0d3e89","observation_id":"5ac3ad9e-4142-4e43-adea-ed6b1cd9ec4d","resolution":{"observed_at":"2026-08-03T15:10:20.696979Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.13746","last_updated":"2022-03-30T14:45:58Z","snapshot_observed_at":"2026-08-16T20:48:04.739588Z","submitted_at":"2022-03-25T16:23:02Z","title":"Code Smells for Machine Learning Applications","version":2},"cited_work":{"arxiv_id":"2203.13746","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2203.13746","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"https://arxiv.org/abs/2203.13746","venue":null,"work_id":"ec0a47cb-680d-4ab1-bec1-542eca263f97","year":2022},"citing_paper":{"arxiv_id":"2605.22976","last_updated":"2026-05-21T19:10:08Z","snapshot_observed_at":"2026-08-11T14:04:04.272329Z","submitted_at":"2026-05-21T19:10:08Z","title":"LLM Code Smells: A Taxonomy and Detection Approach","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-25T05:44:06.132444Z"},"links":{"cited_paper":"/paper/2203.13746","citing_paper":"/paper/2605.22976"},"observation_digest":"sha256:79f97f37cfacc6aaaee355d427c84f2d11ba648d5eea237f6e136da1343f7add","observation_id":"9a53e798-6f68-43a1-a1fe-12288a7b1672","resolution":{"observed_at":"2026-05-25T05:45:24.046547Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2203.13746/citation-record","integrity":"/paper/2203.13746/integrity","json":"/paper/2203.13746/citation-record.json","paper":"/paper/2203.13746"},"outbound":[],"paper":{"arxiv_id":"2203.13746","last_updated":"2022-03-30T14:45:58Z","latest_version":2,"primary_category":"cs.SE","snapshot_observed_at":"2026-08-16T20:48:04.739588Z","submitted_at":"2022-03-25T16:23:02Z","title":"Code Smells for Machine Learning Applications"},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2203.13746."}