{"as_of":"2026-08-10T05:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a9c81a1fe2dc0a2b850aed3309fe3df15da559e975a641e3e917289207d1ec0a","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":"2026-08-09T06:31:02.800959+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":"2026-08-09T22:45:45.439047Z","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":"2026-05-23T03:12:28.770401Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2408.11854","last_updated":"2024-09-19T22:19:28Z","snapshot_observed_at":"2026-08-07T08:53:07.048862Z","submitted_at":"2024-08-15T03:56:40Z","title":"When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.11854","snapshot_observed_at":"2026-08-09T22:45:45.439047Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.18724","last_updated":"2025-09-04T19:36:17Z","snapshot_observed_at":"2026-08-09T22:40:13.598951Z","submitted_at":"2025-01-30T19:58:45Z","title":"Large Language Models with Temporal Reasoning for Longitudinal Clinical Summarization and Prediction","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-09T22:45:45.439047Z"},"links":{"cited_paper":"/paper/2408.11854","citing_paper":"/paper/2501.18724"},"observation_digest":"sha256:65404655970be539c168f428fed4416c87ca24f5fd84716d409aea7800b76eba","observation_id":"b6439733-5280-432d-882c-c5d23bb9b936","resolution":{"observed_at":"2026-08-09T22:45:45.439047Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.11854","last_updated":"2024-09-19T22:19:28Z","snapshot_observed_at":"2026-08-07T08:53:07.048862Z","submitted_at":"2024-08-15T03:56:40Z","title":"When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?","version":2},"cited_work":{"arxiv_id":"2408.11854","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2408.11854","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"67fa9a19-5cf0-4c2b-8f04-402ff8db78c1","year":2024},"citing_paper":{"arxiv_id":"2502.09741","last_updated":"2026-04-21T01:47:04Z","snapshot_observed_at":"2026-07-31T13:50:10.330381Z","submitted_at":"2025-02-13T19:54:59Z","title":"FoNE: Precise Single-Token Number Embeddings via Fourier Features","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-23T03:07:37.363965Z"},"links":{"cited_paper":"/paper/2408.11854","citing_paper":"/paper/2502.09741"},"observation_digest":"sha256:edcb6cf26f220b19223002621bf90e69cd45e3034e6b4bbb64b3210eafdfac2d","observation_id":"8b5f9c3f-74c8-4e18-9f95-f9fa4d1172ee","resolution":{"observed_at":"2026-05-23T03:12:28.772920Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2408.11854","last_updated":"2024-09-19T22:19:28Z","snapshot_observed_at":"2026-08-07T08:53:07.048862Z","submitted_at":"2024-08-15T03:56:40Z","title":"When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.11854","snapshot_observed_at":"2026-08-07T11:32:31.651069Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.06355","last_updated":"2025-06-02T22:07:53Z","snapshot_observed_at":"2026-08-09T09:12:01.361746Z","submitted_at":"2025-06-02T22:07:53Z","title":"LLMs as World Models: Data-Driven and Human-Centered Pre-Event Simulation for Disaster Impact Assessment","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-07T11:32:31.651069Z"},"links":{"cited_paper":"/paper/2408.11854","citing_paper":"/paper/2506.06355"},"observation_digest":"sha256:951209f3d313294c8860d3b2a6944b7b80061427197c0380957348473bde5f7e","observation_id":"e1b50853-e204-44b5-864d-d581fed0823b","resolution":{"observed_at":"2026-08-07T11:32:31.651069Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2408.11854/citation-record","integrity":"/paper/2408.11854/integrity","json":"/paper/2408.11854/citation-record.json","paper":"/paper/2408.11854"},"outbound":[],"paper":{"arxiv_id":"2408.11854","last_updated":"2024-09-19T22:19:28Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-07T08:53:07.048862Z","submitted_at":"2024-08-15T03:56:40Z","title":"When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical 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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2408.11854."}