{"as_of":"2026-08-09T17:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:74ff7fd8d0e3a558e71ca4dccb5938ef3e27d6ceeda438fe6bad409bfbbdecdb","coverage":[{"denominator":27,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":27,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T16:23:34.971296Z","state":"measured"},{"denominator":27,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":27,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2507.13655/citation-record","integrity":"/paper/2507.13655/integrity","json":"/paper/2507.13655/citation-record.json","paper":"/paper/2507.13655"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:38.522585Z","title":"Introducing claude: An ai assistant built with safety and reliability","venue":null,"work_id":"3df500c1-c927-4458-90e2-140972a5bebc","year":2023},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:32.577662Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:5c9b395369e39c58e602b0b8b573cfc99248184a427a2fef855272546cfe7709","observation_id":"52d11bf4-a80c-43e3-9961-3030335684b5","resolution":{"observed_at":"2026-08-06T16:23:38.612774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:38.350767Z","title":"Patient subtyping via time-aware lstm networks","venue":null,"work_id":"0c513c9b-af6e-4876-a40a-9497de93edac","year":2017},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:32.671180Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:2d2cd2f9552e5df9aace9a22c0c48f67d750db319421176788bf3124350d4bcb","observation_id":"f2eb328a-8eb0-4b08-b4ab-982a70d68bf5","resolution":{"observed_at":"2026-08-06T16:23:38.418716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2303.11557","last_updated":"2023-03-21T02:54:35Z","snapshot_observed_at":"2026-07-06T15:06:00.970408Z","submitted_at":"2023-03-21T02:54:35Z","title":"MWAX: A New Correlator for the Murchison Widefield Array","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.11557","snapshot_observed_at":"2026-08-06T16:23:32.761707Z","title":"Clinicalgpt: A llm for healthcare domain","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:32.761707Z"},"links":{"cited_paper":"/paper/2303.11557","citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:5dcbf6150b4a3ff15d7c69083907082efaa387fe2f914703a0de4a2622116a28","observation_id":"fb4538d1-82bc-41d9-9e2e-e5ffc733a765","resolution":{"observed_at":"2026-08-06T16:23:32.761707Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:38.188475Z","title":"Language models are few-shot learners","venue":null,"work_id":"11427a7a-426b-40ed-8957-b5b48be9bdbd","year":1901},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:32.868388Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:9292db3bcdb75d341539251d671807a55c4a44143d6ea4c053bdcdefb002fce0","observation_id":"fd82828b-012f-44b5-b1c9-70b62c58345e","resolution":{"observed_at":"2026-08-06T16:23:38.295138Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2204.02311","last_updated":"2022-10-05T06:02:24Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2022-04-05T16:11:45Z","title":"PaLM: Scaling Language Modeling with Pathways","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.02311","snapshot_observed_at":"2026-08-06T16:23:32.963804Z","title":"Palm: Scaling language modeling with pathways","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:32.963804Z"},"links":{"cited_paper":"/paper/2204.02311","citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:b9ba3e1bd647990a588076d627423aa58ebf05f3410cf95e78b3e0d212e22f1e","observation_id":"b1c351a8-cf8a-4023-946b-2751f5d02f1f","resolution":{"observed_at":"2026-08-06T16:23:32.963804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:37.968959Z","title":"Scaling instruction-finetuned language models","venue":null,"work_id":"7c07c4c8-035e-4f23-a590-cc10fa91942b","year":2022},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.054429Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:2188cdc8ee3ed8b6d0e508e3c047b14c18bd998aa586e735a1f6016867bfe3a3","observation_id":"2bb0b185-acf4-416b-acdb-5325c63ab0ec","resolution":{"observed_at":"2026-08-06T16:23:38.072953Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:37.787731Z","title":"Domain-specific language model pretraining for biomedical natural language processing","venue":null,"work_id":"f5d7b5b6-9565-4944-973d-77f2ede6aecf","year":2021},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.118532Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:6ad8e567f119d6624e8d1e2217f2e754d6014fa748b99ed8409a5bd8d534d4fa","observation_id":"6b5aea3b-3158-4a54-8910-4ef7a73ea7b4","resolution":{"observed_at":"2026-08-06T16:23:37.883090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:37.546158Z","title":"Parameter-efficient transfer learning with adaptive attention","venue":null,"work_id":"ff2addd3-0a3e-4565-a052-c2febbf275a7","year":2022},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.160111Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:dbb484eb1a6530e25ffa60ee17a0d8ad50813779def3c3ad4b7b3a5cecafd51d","observation_id":"f3369049-9a13-4ce0-991d-a994dff17620","resolution":{"observed_at":"2026-08-06T16:23:37.670987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:37.287822Z","title":"Multitask learning and benchmarking with clinical time series data","venue":null,"work_id":"2aa4c74f-396e-477e-86da-6032effd9590","year":2019},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.237057Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:3f8779ce1224cfc286f7d27fae77c5ce8ab68d916564711921fd09f16a7b5b08","observation_id":"d18e75c3-915b-4849-8c60-2ae6488c81f2","resolution":{"observed_at":"2026-08-06T16:23:37.415831Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:37.004496Z","title":"Lora: Low-rank adaptation of large language models","venue":null,"work_id":"4613c2c5-03f1-4453-9636-75a67ee0fcdd","year":2021},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.324138Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:416af21314eb5455f834cb5da8b25d09cd36ef95caf31dba8ddf812581d8ea32","observation_id":"a41cc5da-314f-4e0c-9186-110327a113be","resolution":{"observed_at":"2026-08-06T16:23:37.133819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:36.761211Z","title":"Clinicalbert: Modeling clinical notes and predicting hospital readmission","venue":null,"work_id":"8dee358e-300d-42fc-bd25-2479b3809adb","year":2019},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.384684Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:681b9cde2dffda3d842a7f0421aa23df773e2aab3c2ab1314691f205d0e986ff","observation_id":"2a18d500-4a0f-4e6b-9b02-329334bdc41a","resolution":{"observed_at":"2026-08-06T16:23:36.869592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2106.11512","last_updated":"2021-06-22T03:00:11Z","snapshot_observed_at":"2026-07-06T11:21:34.268494Z","submitted_at":"2021-06-22T03:00:11Z","title":"An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN","version":1},"cited_work":{"arxiv_id":"2106.11512","doi":null,"metadata_source":"pith","pith_arxiv_id":"2106.11512","snapshot_observed_at":"2026-08-06T16:23:35.293325Z","title":"An Accurate Non-accelerometer-based PPG Motion Artifact Removal Technique using CycleGAN","venue":"cs.LG","work_id":"ea6afec1-5ddf-4e46-9935-b8c2060cb5e7","year":2021},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.474211Z"},"links":{"cited_paper":"/paper/2106.11512","citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:ccb5e6ba696c6c4513a5569e46f6205908ebff4030c3f83afe61893dd0cffad6","observation_id":"70a5aa5a-5bdd-4774-a4da-bcb3f0eb7cb0","resolution":{"observed_at":"2026-08-06T16:23:35.349897Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:36.543674Z","title":"Biobert: a pre-trained biomedical language representation model for biomedical text mining","venue":null,"work_id":"314f2856-1b4b-4d04-b799-11f059838a66","year":2020},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.645869Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:da61e7447eb18d91fd6ea650efd069e663796dc1b5c4d448602d1ae61c663267","observation_id":"bdce2dec-9f5c-4475-b037-01c3c3a2bb88","resolution":{"observed_at":"2026-08-06T16:23:36.656127Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2104.08691","last_updated":"2021-09-02T17:34:41Z","snapshot_observed_at":"2026-08-06T15:24:34.790850Z","submitted_at":"2021-04-18T03:19:26Z","title":"The Power of Scale for Parameter-Efficient Prompt Tuning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.08691","snapshot_observed_at":"2026-08-06T16:23:33.726857Z","title":"The power of scale for parameter-efficient prompt tuning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.726857Z"},"links":{"cited_paper":"/paper/2104.08691","citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:220509212132c56b5b9c7f6b411cdcfac366ba3ac683a6a62645d6f1e8e50ae9","observation_id":"912e8156-3b04-4b10-bee7-d6b53bf93e73","resolution":{"observed_at":"2026-08-06T16:23:33.726857Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2107.13586","last_updated":"2021-07-28T18:09:46Z","snapshot_observed_at":"2026-07-06T11:33:25.933445Z","submitted_at":"2021-07-28T18:09:46Z","title":"Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.13586","snapshot_observed_at":"2026-08-06T16:23:33.783142Z","title":"Pre-train prompt for few-shot learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.783142Z"},"links":{"cited_paper":"/paper/2107.13586","citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:1b59a08d4df064806e557cb54a0a989e1ec66c12d590ba75ad6dbf29a612bb69","observation_id":"33c65180-c3eb-4881-86f4-6500d9781ced","resolution":{"observed_at":"2026-08-06T16:23:33.783142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:36.413256Z","title":"nbertscore: Evaluating clinical note generation with semantic and clinical similarity","venue":null,"work_id":"290da78b-20c3-4e98-bb2d-26af66795f7a","year":2022},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.835087Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:36abaa05c332554315b548266c7a8c9dd6e2ecac43575fd69a815c81139e76c7","observation_id":"47a5cec3-947e-48e2-9d1a-672e2ba4aa83","resolution":{"observed_at":"2026-08-06T16:23:36.470826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2303.17580","last_updated":"2023-12-03T18:17:21Z","snapshot_observed_at":"2026-08-03T00:52:54.308486Z","submitted_at":"2023-03-30T17:48:28Z","title":"HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.17580","snapshot_observed_at":"2026-08-06T16:23:33.904262Z","title":"Medalpaca: Finetuning llms on medical instruction datasets","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.904262Z"},"links":{"cited_paper":"/paper/2303.17580","citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:8c700056e22d9d49c26de211c1a7bd83d0ece65e7a4eaf5210e7cc0d5a7dba71","observation_id":"855109cd-8d4e-4b1d-9218-25228edbe8f4","resolution":{"observed_at":"2026-08-06T16:23:33.904262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:36.283550Z","title":"An interpretable machine learning model for accurate prediction of sepsis in the icu","venue":null,"work_id":"728910c8-7e80-4113-8a74-e5fe3a492b7e","year":2018},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:33.974267Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:55bc2d8a544591e0ce6b6e11950f4ad41e6be2e3b5a2a60d759f2f607a03c136","observation_id":"d1688e16-32aa-49ec-87b3-af40e9184e31","resolution":{"observed_at":"2026-08-06T16:23:36.349110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-06T16:23:34.104281Z","title":"Gpt-4 technical report","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:34.104281Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:ec5b48d0dcc1c21bfad3ed852533241f77e7d2990f23b9cf2d07d913cd034175","observation_id":"7ffdd692-f5ab-4615-bc70-9f2ebab43c81","resolution":{"observed_at":"2026-08-06T16:23:34.104281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.11939","last_updated":"2022-10-21T13:13:40Z","snapshot_observed_at":"2026-08-03T05:27:16.958811Z","submitted_at":"2022-10-21T13:13:40Z","title":"Automatic Cattle Identification using YOLOv5 and Mosaic Augmentation: A Comparative Analysis","version":1},"cited_work":{"arxiv_id":"2210.11939","doi":null,"metadata_source":"pith","pith_arxiv_id":"2210.11939","snapshot_observed_at":"2026-08-06T16:23:35.064546Z","title":"Automatic Cattle Identification using YOLOv5 and Mosaic Augmentation: A Comparative Analysis","venue":"cs.CV","work_id":"8161baf7-16ff-4da2-9ed4-d36fa2dcf582","year":2022},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:34.258789Z"},"links":{"cited_paper":"/paper/2210.11939","citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:84cf5b2754f129a9c33215ac9cae1c2845b27289219802bb6404e4292d027cd6","observation_id":"1e36079a-fb16-45cb-b742-2acd30678134","resolution":{"observed_at":"2026-08-06T16:23:35.134733Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:34.400474Z","title":"Exploring the limits of transfer learning with a unified text-to-text transformer","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:34.400474Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:913f9a7a95182a5446fba5161d0bb1a86dee640ef218f19d15f9ee47f231407f","observation_id":"59ef9a18-ab61-4626-ab9a-6e557afa2618","resolution":{"observed_at":"2026-08-06T16:23:34.400474Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:36.091413Z","title":"Scalable and accurate deep learning with electronic health records","venue":null,"work_id":"793a46e1-cfea-46c8-bf5c-b2d34b9077cd","year":2018},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:34.561520Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:d7324f2922a8e2c68302904aad43234c21ee14818f0abb2e5a5dde627ecd9e06","observation_id":"83abf597-b431-489b-a135-bc56e7c37fbd","resolution":{"observed_at":"2026-08-06T16:23:36.178698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:35.874677Z","title":"Introducing gemini: Google’s next-generation ai model","venue":null,"work_id":"9a613a82-613b-47df-8211-f4d0317fa2f2","year":2024},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:34.668962Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:64b41fb60c0f3b3ce6b5f1ea5ca3f64ae013d730aff918789160e24666d4d0b0","observation_id":"28173b5b-a143-4751-813e-58bd355da094","resolution":{"observed_at":"2026-08-06T16:23:36.004468Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:35.729964Z","title":"Large language models encode clinical knowledge","venue":null,"work_id":"920113f4-5155-4ac1-9f29-f6a692734256","year":2023},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:34.751261Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:15cb26c3f259d0b381d1bb192abece61b5ac5a776790168a8f66fc138d7fab91","observation_id":"761746d3-5839-4e14-8452-8fa8d77543c5","resolution":{"observed_at":"2026-08-06T16:23:35.773223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:35.576145Z","title":"Adalora: Adaptive low-rank adaptation for efficient fine-tuning of large language models","venue":null,"work_id":"6db91578-f643-4ffc-b12c-26045a7fa2f6","year":2022},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:34.819211Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:b55646e66f3ab6486e3506ee01c24bcf5367d2a02c038ac6c019d2721c7c6142","observation_id":"acae24e0-f36e-4b9c-bf59-4d1e49149190","resolution":{"observed_at":"2026-08-06T16:23:35.642544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T16:23:35.458611Z","title":"Transform- ers: State-of-the-art natural language processing","venue":null,"work_id":"09aae258-51a2-4a4b-99a3-efacb85b9e5b","year":2020},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:34.887346Z"},"links":{"citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:3fe6ec653647801b841f85f8cb943784deadb7ce3b5a28a40537155c37ce8cef","observation_id":"1b11b934-2ad1-4d1c-a0c3-c760fd1086c2","resolution":{"observed_at":"2026-08-06T16:23:35.507410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2305.17826","last_updated":"2023-05-28T23:35:17Z","snapshot_observed_at":"2026-07-06T15:34:33.026657Z","submitted_at":"2023-05-28T23:35:17Z","title":"NOTABLE: Transferable Backdoor Attacks Against Prompt-based NLP Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.17826","snapshot_observed_at":"2026-08-06T16:23:34.971296Z","title":"Adaptive low-rank adaptation for efficient fine-tuning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T16:23:34.971296Z"},"links":{"cited_paper":"/paper/2305.17826","citing_paper":"/paper/2507.13655"},"observation_digest":"sha256:fbd21418168ffa2baa9460e2e926a907bc84bf6c81b54377d97cb9db7f16cf17","observation_id":"ee6754aa-73eb-4636-b2a7-cb046b0668e8","resolution":{"observed_at":"2026-08-06T16:23:34.971296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.13655","last_updated":"2025-07-18T04:49:41Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-06T16:16:37.163278Z","submitted_at":"2025-07-18T04:49:41Z","title":"CU-ICU: Customizing Unsupervised Instruction-Finetuned Language Models for ICU Datasets via Text-to-Text Transfer Transformer"},"reference_resolution":{"displayed":27,"state_counts":{"malformed_identifier":0,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":8,"verified_exact":0,"verified_fuzzy":17},"total_outbound_references":27},"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 9 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2507.13655."}