{"as_of":"2026-08-15T06:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0742e21d48458057fb5c5dfb27bca06ab7d90c14662c49cc93219bb19a458e00","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":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-14T06:32:32.682623+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T23:24:46.108719Z","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":"2026-08-06T23:50:42.749860Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2310.14478","last_updated":"2023-10-23T01:20:01Z","snapshot_observed_at":"2026-08-13T05:43:35.682916Z","submitted_at":"2023-10-23T01:20:01Z","title":"GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.14478","snapshot_observed_at":"2026-08-10T23:24:46.108719Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.20414","last_updated":"2024-12-29T09:47:14Z","snapshot_observed_at":"2026-08-14T04:50:12.102964Z","submitted_at":"2024-12-29T09:47:14Z","title":"Comparative Performance of Advanced NLP Models and LLMs in Multilingual Geo-Entity Detection","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T23:24:46.108719Z"},"links":{"cited_paper":"/paper/2310.14478","citing_paper":"/paper/2412.20414"},"observation_digest":"sha256:2ae34783844e29713a12bfca2e3d9385f951bed741c319ca75e92c07059f3b46","observation_id":"7034b474-8541-425a-9c09-b0e310588d4f","resolution":{"observed_at":"2026-08-10T23:24:46.108719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.14478","last_updated":"2023-10-23T01:20:01Z","snapshot_observed_at":"2026-08-13T05:43:35.682916Z","submitted_at":"2023-10-23T01:20:01Z","title":"GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.14478","snapshot_observed_at":"2026-08-09T14:03:40.119117Z","title":"Geolm: Empowering language models for geospatially grounded language understanding","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.18470","last_updated":"2025-06-11T04:41:29Z","snapshot_observed_at":"2026-08-14T02:38:58.792286Z","submitted_at":"2025-02-04T01:30:06Z","title":"Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questions","version":5},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T14:03:40.119117Z"},"links":{"cited_paper":"/paper/2310.14478","citing_paper":"/paper/2502.18470"},"observation_digest":"sha256:e883f4bd0c84db3d05e0e1c47163b3b2ef27d4de9ab540822d83c9d183e87cf7","observation_id":"e318a4df-148e-4807-b3ad-e614f394e7b9","resolution":{"observed_at":"2026-08-09T14:03:40.119117Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.14478","last_updated":"2023-10-23T01:20:01Z","snapshot_observed_at":"2026-08-13T05:43:35.682916Z","submitted_at":"2023-10-23T01:20:01Z","title":"GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.14478","snapshot_observed_at":"2026-08-07T00:22:19.643451Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.14345","last_updated":"2025-06-17T09:38:45Z","snapshot_observed_at":"2026-08-13T04:00:57.374755Z","submitted_at":"2025-06-17T09:38:45Z","title":"A Vision for Geo-Temporal Deep Research Systems: Towards Comprehensive, Transparent, and Reproducible Geo-Temporal Information Synthesis","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T00:22:19.643451Z"},"links":{"cited_paper":"/paper/2310.14478","citing_paper":"/paper/2506.14345"},"observation_digest":"sha256:b925769fc37d664beb8b79530b0b71d39d748b8e1ae40f3891567fb402f6717f","observation_id":"5fc00aa5-7e7c-4374-85d7-f02dc4ee81ab","resolution":{"observed_at":"2026-08-07T00:22:19.643451Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.14478","last_updated":"2023-10-23T01:20:01Z","snapshot_observed_at":"2026-08-13T05:43:35.682916Z","submitted_at":"2023-10-23T01:20:01Z","title":"GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding","version":1},"cited_work":{"arxiv_id":"2310.14478","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.14478","snapshot_observed_at":"2026-08-06T23:50:42.749860Z","title":"GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding","venue":"cs.CL","work_id":"99e2ba28-9f56-4b60-aeaf-f6ec96d4ee88","year":2023},"citing_paper":{"arxiv_id":"2506.16006","last_updated":"2025-06-19T03:51:47Z","snapshot_observed_at":"2026-08-15T02:04:20.869095Z","submitted_at":"2025-06-19T03:51:47Z","title":"DIGMAPPER: A Modular System for Automated Geologic Map Digitization","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T23:50:42.408373Z"},"links":{"cited_paper":"/paper/2310.14478","citing_paper":"/paper/2506.16006"},"observation_digest":"sha256:3b9ce9f672cbff066f85be3bc7fdb93bde44de012765b56ee44500520f3dddaf","observation_id":"71cd1ebc-030a-4c1e-8096-cd4752f5bb64","resolution":{"observed_at":"2026-08-06T23:50:42.757926Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2310.14478/citation-record","integrity":"/paper/2310.14478/integrity","json":"/paper/2310.14478/citation-record.json","paper":"/paper/2310.14478"},"outbound":[],"paper":{"arxiv_id":"2310.14478","last_updated":"2023-10-23T01:20:01Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-13T05:43:35.682916Z","submitted_at":"2023-10-23T01:20:01Z","title":"GeoLM: Empowering Language Models for Geospatially Grounded Language Understanding"},"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":"2026-08-14T06:32:32.682623+00:00","source":"crossref"},{"observed_at":"2026-08-14T06:32:18.44784+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2310.14478."}