{"as_of":"2026-08-12T18:53:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:434067fbbf261d924b7024b54b00a791b35a596a2ac2a2211b8c6b3c7927e032","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":7,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":7,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+00:00","state":"measured"},{"denominator":7,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":7,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T04:09:36.680792Z","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":"2026-05-13T05:53:22.146453Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2007.15779","last_updated":"2021-09-16T21:26:07Z","snapshot_observed_at":"2026-08-03T16:32:01.630626Z","submitted_at":"2020-07-31T00:04:15Z","title":"Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing","version":6},"cited_work":{"arxiv_id":"2007.15779","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2007.15779","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Domain-specific language model pretraining for biomedical natural language processing","venue":null,"work_id":"68fd6571-0cf4-47ff-9822-aee39142a54b","year":2007},"citing_paper":{"arxiv_id":"2211.09085","last_updated":"2022-11-16T18:06:33Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-11-16T18:06:33Z","title":"Galactica: A Large Language Model for Science","version":1},"reference_index":169,"source":"arxiv_source","source_observed_at":"2026-05-13T05:53:21.810346Z"},"links":{"cited_paper":"/paper/2007.15779","citing_paper":"/paper/2211.09085"},"observation_digest":"sha256:27b12f7801566f1bdd648fe8b55c6d090ab3d4bae16c4e989ed48238f95ff40b","observation_id":"152d230a-eb7a-4367-889b-b920c326186e","resolution":{"observed_at":"2026-05-13T05:53:22.148464Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2007.15779","last_updated":"2021-09-16T21:26:07Z","snapshot_observed_at":"2026-08-03T16:32:01.630626Z","submitted_at":"2020-07-31T00:04:15Z","title":"Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.15779","snapshot_observed_at":"2026-08-07T04:09:36.680792Z","title":"Domain-specific language model 36 pretraining for biomedical natural language processing, 2020","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.17277","last_updated":"2025-06-13T07:44:53Z","snapshot_observed_at":"2026-08-10T08:09:18.790353Z","submitted_at":"2025-06-13T07:44:53Z","title":"Chunk Twice, Embed Once: A Systematic Study of Segmentation and Representation Trade-offs in Chemistry-Aware Retrieval-Augmented Generation","version":1},"reference_index":116,"source":"pdf_text","source_observed_at":"2026-08-07T04:09:36.680792Z"},"links":{"cited_paper":"/paper/2007.15779","citing_paper":"/paper/2506.17277"},"observation_digest":"sha256:89406a81a58b918585c5a837a7e2b0ca05e8c131ae3a7a891a01caa5de5f22eb","observation_id":"41474384-c8d0-4d91-aa7f-7d9338d4b877","resolution":{"observed_at":"2026-08-07T04:09:36.680792Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.15779","last_updated":"2021-09-16T21:26:07Z","snapshot_observed_at":"2026-08-03T16:32:01.630626Z","submitted_at":"2020-07-31T00:04:15Z","title":"Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.15779","snapshot_observed_at":"2026-08-06T22:45:51.914294Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.20844","last_updated":"2025-06-29T02:57:01Z","snapshot_observed_at":"2026-08-10T13:25:37.996858Z","submitted_at":"2025-06-25T21:29:33Z","title":"The Next Phase of Scientific Fact-Checking: Advanced Evidence Retrieval from Complex Structured Academic Papers","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T22:45:51.914294Z"},"links":{"cited_paper":"/paper/2007.15779","citing_paper":"/paper/2506.20844"},"observation_digest":"sha256:d2f7c88e05b9b91554822eccb753f05a19f4d9a7242ed95bddd63f225ff44846","observation_id":"200c5f49-d374-4d0c-9c68-a8511aac15db","resolution":{"observed_at":"2026-08-06T22:45:51.914294Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.15779","last_updated":"2021-09-16T21:26:07Z","snapshot_observed_at":"2026-08-03T16:32:01.630626Z","submitted_at":"2020-07-31T00:04:15Z","title":"Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.15779","snapshot_observed_at":"2026-08-06T16:12:42.038846Z","title":"Domain-specific language model pretraining for biomedical natural language processing","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2507.14374","last_updated":"2025-07-18T21:41:20Z","snapshot_observed_at":"2026-08-09T12:18:45.908623Z","submitted_at":"2025-07-18T21:41:20Z","title":"Error-Aware Curriculum Learning for Biomedical Relation Classification","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T16:12:42.038846Z"},"links":{"cited_paper":"/paper/2007.15779","citing_paper":"/paper/2507.14374"},"observation_digest":"sha256:a6935c9521fcde3a11658321023e94608bd317c9fa7b503771caadb4548f8487","observation_id":"a4a4a07a-3d57-44b4-8036-d74c4364bb3a","resolution":{"observed_at":"2026-08-06T16:12:42.038846Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.15779","last_updated":"2021-09-16T21:26:07Z","snapshot_observed_at":"2026-08-03T16:32:01.630626Z","submitted_at":"2020-07-31T00:04:15Z","title":"Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.15779","snapshot_observed_at":"2026-08-06T05:37:42.663453Z","title":"Gu , author R","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.01480","last_updated":"2025-08-02T20:20:08Z","snapshot_observed_at":"2026-08-08T21:33:44.533233Z","submitted_at":"2025-08-02T20:20:08Z","title":"Harnessing Collective Intelligence of LLMs for Robust Biomedical QA: A Multi-Model Approach","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-06T05:37:42.663453Z"},"links":{"cited_paper":"/paper/2007.15779","citing_paper":"/paper/2508.01480"},"observation_digest":"sha256:8a5598a1f08775989395b48cecb5fe4a2de8b12f1889969a7a626df8f52677de","observation_id":"af9ad4fe-bcf7-4981-bc07-08735745e41b","resolution":{"observed_at":"2026-08-06T05:37:42.663453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.15779","last_updated":"2021-09-16T21:26:07Z","snapshot_observed_at":"2026-08-03T16:32:01.630626Z","submitted_at":"2020-07-31T00:04:15Z","title":"Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.15779","snapshot_observed_at":"2026-08-04T20:43:57.694737Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2509.09725","last_updated":"2025-09-10T09:14:25Z","snapshot_observed_at":"2026-08-10T13:13:15.439816Z","submitted_at":"2025-09-10T09:14:25Z","title":"BIBERT-Pipe on Biomedical Nested Named Entity Linking at BioASQ 2025","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-04T20:43:57.694737Z"},"links":{"cited_paper":"/paper/2007.15779","citing_paper":"/paper/2509.09725"},"observation_digest":"sha256:fd670ab2700f6ecbc451cc3836c75cf977b29d048fd37120f3eb5aa59b33485c","observation_id":"ed58f0d7-ed32-49d9-baf3-1934c255a711","resolution":{"observed_at":"2026-08-04T20:43:57.694737Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2007.15779","last_updated":"2021-09-16T21:26:07Z","snapshot_observed_at":"2026-08-03T16:32:01.630626Z","submitted_at":"2020-07-31T00:04:15Z","title":"Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2007.15779","snapshot_observed_at":"2026-08-01T21:19:32.606606Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2607.19415","last_updated":"2026-07-17T17:11:11Z","snapshot_observed_at":"2026-08-06T10:50:10.835832Z","submitted_at":"2026-07-17T17:11:11Z","title":"Auditing Retrieval-Augmented LLM Hypotheses for Longitudinal Cell Painting Morphology","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T21:19:32.606606Z"},"links":{"cited_paper":"/paper/2007.15779","citing_paper":"/paper/2607.19415"},"observation_digest":"sha256:96f3b9d97c32a7a1561b7d0c01d9fd6c697dc2bf9327db9e3412f65091e93db2","observation_id":"730c3e1b-2aa5-42b9-b4c3-905212387562","resolution":{"observed_at":"2026-08-01T21:19:32.606606Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2007.15779/citation-record","integrity":"/paper/2007.15779/integrity","json":"/paper/2007.15779/citation-record.json","paper":"/paper/2007.15779"},"outbound":[],"paper":{"arxiv_id":"2007.15779","last_updated":"2021-09-16T21:26:07Z","latest_version":6,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-03T16:32:01.630626Z","submitted_at":"2020-07-31T00:04:15Z","title":"Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing"},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2007.15779."}