{"as_of":"2026-08-22T14:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e730243815663405ab426b910febd1426c53175de997641bc7d846643d3f9e35","coverage":[{"denominator":69,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":69,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T22:50:02.481584Z","state":"measured"},{"denominator":69,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":69,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+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/2501.18714/citation-record","integrity":"/paper/2501.18714/integrity","json":"/paper/2501.18714/citation-record.json","paper":"/paper/2501.18714"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:50:03.359916Z","title":"Programming cellular alignment in engineered cardiac tissue via bioprinting anisotropic organ building blocks","venue":null,"work_id":"dc2cba09-49db-4ec2-9a16-bb0c098b8b6f","year":2022},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.254641Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:460dd254dba0b7dd55ebccb451b2c9db8ebd342e6b2f5ebfbd84f001bb3f7a5e","observation_id":"b4d9557f-1bc1-4a92-937d-7dda081afda6","resolution":{"observed_at":"2026-08-09T22:50:03.363645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.349385Z","title":"Decoding rejuvenating effects of mechanical loading on skeletal aging using in vivo µct imaging and deep learning","venue":null,"work_id":"82e015e1-b740-4bd2-b54b-2c947cef757a","year":2020},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.258808Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:0bf9678432a40d303542d37ad5837f65f4e3f421b57330518c0f0a3a01aa959e","observation_id":"a4c52ee0-a11c-4f4f-9d33-a9c779f82828","resolution":{"observed_at":"2026-08-09T22:50:03.353146Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.328734Z","title":"Sarcomere alignment is regulated by myocyte shape","venue":null,"work_id":"bdd9f1ff-c94c-4cae-8cbe-e308cb187104","year":2008},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.262539Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:7ac39174e2cfa14875a43108a6c4115a999a69f7358934364d6b2b6d3ef76ab4","observation_id":"18e455bb-85c0-4d58-9038-9b29d37cb867","resolution":{"observed_at":"2026-08-09T22:50:03.332725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.317591Z","title":"Measurement and analysis of sarcomere length in rat cardiomyocytes in situ and in vitro.American Journal of Physiology-Heart and Circulatory Physiology, 298(5):H1616–H1625, 2010","venue":null,"work_id":"e83e54f0-00c2-4fef-8dba-7b84db8d33df","year":2010},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.266382Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:9775108cb95d104ebda65a79b7bcbbeaada5761d0ba2bd5bc9dff9ea6061abe3","observation_id":"665ae620-171e-4b76-ba55-5363e9b6b101","resolution":{"observed_at":"2026-08-09T22:50:03.322095Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.307783Z","title":"Automated image analysis system for studying cardiotoxicity in human pluripotent stem cell-derived cardiomyocytes","venue":null,"work_id":"c388306e-aedc-4a72-b925-e3d1eee4fcc4","year":2020},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.269722Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:b02ff2905ec1c991efc4578c5bdde783a964769572660607953f2d8d2599f74e","observation_id":"51d7e01d-c46e-4cce-ac0a-52e644078582","resolution":{"observed_at":"2026-08-09T22:50:03.311368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.298104Z","title":"Automated sarcomere structure analysis for studying cardiotoxicity in human pluripotent stem cell-derived cardiomyocytes","venue":null,"work_id":"2e6e2d46-d163-4af1-b950-74953e3c9907","year":2023},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.273144Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:81f76a78bb75097a851c5acf24517eb6dd95894dae1efc2adf0deb648d0ef7f1","observation_id":"ab199d79-a111-4bf5-b7ab-2e0381e93257","resolution":{"observed_at":"2026-08-09T22:50:03.301611Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.287590Z","title":"Cellprofiler: image analysis software for identifying and quantifying cell phenotypes","venue":null,"work_id":"afa02a5e-bc18-4c2b-8c99-5dcf820023cf","year":2006},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.276844Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:0637941664863a746b6b28f1b4ec7593e12704a34ed30a5d59d4349ef77ff8d6","observation_id":"64af0c93-6a1d-4783-b630-43409cbff11b","resolution":{"observed_at":"2026-08-09T22:50:03.291481Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.277083Z","title":"Deep learning for cardiac image segmentation: a review","venue":null,"work_id":"5cbc9db5-f1d2-406c-9cec-136c0fd0962f","year":2020},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.280092Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:1b6e8ba5db51de0f59b61d00d84cfa530b813fe25bfd61c6aa3ca2e7db1a975f","observation_id":"f6e50cd6-7bc9-477f-a4ee-139a86d64170","resolution":{"observed_at":"2026-08-09T22:50:03.280788Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.265553Z","title":"A simple framework for contrastive learning of visual representations","venue":null,"work_id":"b7ed3218-7417-4a93-bab8-57817a131251","year":2020},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.283473Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:14ab325cb7adebbc92f1344b2756519490d59fc292533e9102190311dfddcf99","observation_id":"0a8a62b8-c80a-4d7e-9742-b65c8ddd5316","resolution":{"observed_at":"2026-08-09T22:50:03.269982Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.242300Z","title":"Multimodal object detection via probabilistic ensembling","venue":null,"work_id":"562c6cad-7cc8-4f96-ae34-c8acac328582","year":2022},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.286838Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:17af0848155438c2b53cde55f7ae20c353c0a918b81d8ad01b002241f2bb79eb","observation_id":"96d532d8-061f-4a8c-84ed-b4190f183d5b","resolution":{"observed_at":"2026-08-09T22:50:03.248387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.230836Z","title":"Multiscale characteri- zation of heart failure","venue":null,"work_id":"4f0dc419-17c8-4c47-9bdd-96960fdabf54","year":2019},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.290208Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:52bb0bd9d6d5d0a564a86bafaaa2c4b0077b6bc5ca15cc0dd5d3f664af2fc978","observation_id":"66571397-869a-4d96-80d8-77e795501d36","resolution":{"observed_at":"2026-08-09T22:50:03.234744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.219125Z","title":"Cardiac sarcomere mechanics in health and disease","venue":null,"work_id":"0ed8b1aa-6bfb-487a-ab98-c99b52b56131","year":2021},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.293778Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:26be123227102c1a66c49553e4b196826744608ef9fa738e5219ae26b4b61398","observation_id":"3aa1215b-35bb-49d8-b3f0-e621e758b3f3","resolution":{"observed_at":"2026-08-09T22:50:03.223357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.297313Z","title":"Imagenet: A large-scale hierarchical image database","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.297313Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:1c49637c10f72e432a66b0a3e77fafe6f1c6d435d0004c5f378e1932b8c96e2f","observation_id":"16a645a5-7c69-4079-8d09-4f41d19ae05d","resolution":{"observed_at":"2026-08-09T22:50:02.297313Z","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":"2026-08-09T22:50:03.203140Z","title":"Machine learning identifies remodeling patterns in human lung extracellular matrix","venue":null,"work_id":"8fc8b447-04c1-4ce3-8559-7cb741954da2","year":2024},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.300636Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:b59a71e8291ab0b0cae6b27a5f40c3070700dcec2104b88b254f1cdd83ac9508","observation_id":"e0094d11-8bc6-464e-bf4d-0e0c6a27c551","resolution":{"observed_at":"2026-08-09T22:50:03.206395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.194152Z","title":"A density-based algorithm for discovering clusters in large spatial databases with noise","venue":null,"work_id":"3ac8781b-4a81-4501-92eb-b74dd161d9db","year":1996},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.303698Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:ece367049e2bfb4f7decc1f7ffd699b347c3c8b5e3f490dcd823c6ff222405d1","observation_id":"68849c80-0acb-4d4b-95f0-a856727afe58","resolution":{"observed_at":"2026-08-09T22:50:03.197493Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.185012Z","title":"Induced pluripotent stem cell-derived cardiomyocyte in vitro models: benchmarking progress and ongoing challenges","venue":null,"work_id":"6d994246-91e5-46ce-b19e-35b540a148f5","year":2024},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.307109Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:9905351826c464330dea8f293cc77668a2a9b0cc27b36d7c75039277169ce470","observation_id":"bcf01f74-40d0-4589-bd6c-0817a3a6b9d0","resolution":{"observed_at":"2026-08-09T22:50:03.188465Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.175253Z","title":"Serial dependence in visual perception","venue":null,"work_id":"22d526e0-b330-430d-848a-1ec5524ffeb1","year":2014},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.310476Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:2473615ad446c50e14fbf17f69d9c710a290a86947ebba87da3c1ff3425f2d2a","observation_id":"ee5806ca-b996-4264-8d81-2741f4e297d6","resolution":{"observed_at":"2026-08-09T22:50:03.178865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.02399","last_updated":"2020-07-02T01:24:51Z","snapshot_observed_at":"2026-08-22T11:38:05.489836Z","submitted_at":"2020-06-03T17:14:55Z","title":"ExKMC: Expanding Explainable $k$-Means Clustering","version":2},"cited_work":{"arxiv_id":"2006.02399","doi":null,"metadata_source":"pith","pith_arxiv_id":"2006.02399","snapshot_observed_at":"2026-08-09T22:50:02.704257Z","title":"ExKMC: Expanding Explainable $k$-Means Clustering","venue":"cs.LG","work_id":"7550a0f9-94ed-4e49-9264-322adc3ebe97","year":2020},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.313586Z"},"links":{"cited_paper":"/paper/2006.02399","citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:2d1afbdd91eea53cd92d9bcec1933167e9c68e980634d3fdc30f98facf80511c","observation_id":"8e8944b9-9484-4a45-ac5a-0047567183ce","resolution":{"observed_at":"2026-08-09T22:50:02.708484Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.164284Z","title":null,"venue":null,"work_id":"8292aec9-10b0-4e9c-9b69-94df1ad0e56c","year":2021},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.317091Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:8e1b84bf461f90dd2186a2f8f7a69fb61db909d5b63fb93bc94d88305041b797","observation_id":"d637a301-8fa8-422f-8a06-d66537485d29","resolution":{"observed_at":"2026-08-09T22:50:03.167881Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.154246Z","title":"Cell states beyond transcriptomics: Integrating structural organization and gene expression in hipsc-derived cardiomyocytes","venue":null,"work_id":"34a76bec-4571-4958-8517-9689f3d29b2d","year":2021},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.320327Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:c1607a9aa76b94ee0dd61c71d62331ef4d761cbaba0a6b184667567400a91c0e","observation_id":"85748c1f-4c9c-42bb-8c52-6959c2612703","resolution":{"observed_at":"2026-08-09T22:50:03.157766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.143154Z","title":"Self-organization of muscle cell structure and function","venue":null,"work_id":"baadbaa6-30b8-48c8-842e-d37ebc39f7e4","year":2011},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.323504Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:ab4a62ab0bed536c4923cf211cc775eb397ed4555429b0314088380b80c6fad9","observation_id":"c00fdfc2-3735-41b8-afdc-181d953d0749","resolution":{"observed_at":"2026-08-09T22:50:03.147405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.132071Z","title":"Statistical and structural approaches to texture","venue":null,"work_id":"8be7a68e-b952-498b-81e9-868c6265e183","year":1979},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.326921Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:1d0f281f456db507b7a7b90354a5e6177e8c5a77c6a5b2671a12976220f33424","observation_id":"c2ba6d00-dcb8-4059-9312-46a8f8d7fe5c","resolution":{"observed_at":"2026-08-09T22:50:03.135934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.121690Z","title":"A data-driven computational model for engineered cardiac microtissues","venue":null,"work_id":"ba91e5bc-6068-4e0a-944b-2dad2dc5d517","year":2023},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.330396Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:27e6b0f4a3f3091645e1b21fe4adcf98e0033b61e3e7642ee5652cc78d5a3b51","observation_id":"7037ad8f-97c8-484a-a8fe-371e8bb8b5b5","resolution":{"observed_at":"2026-08-09T22:50:03.125396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.111937Z","title":"Right ventricular myocardial mechanics: Multi-modal deformation, microstructure, modeling, and comparison to the left ventricle","venue":null,"work_id":"4b50be5f-fd14-4e7a-af52-fa8beacb4483","year":2021},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.333898Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:44737c16e4b0690bad2124ac7151269e652ef79e76a7dc80fd9c2705402a0d4d","observation_id":"09d1ce84-fac9-4698-bf78-33731bd3de78","resolution":{"observed_at":"2026-08-09T22:50:03.115432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.102183Z","title":"Self-supervised deep learning encodes high-resolution features of protein subcellular localization","venue":null,"work_id":"23b58e2f-9b9f-4cce-8570-ba78ea72729b","year":2022},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.337348Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:abf0c87cc6c7816606a8c186cfcd8a3b1a6c722d36d3afa232a9dd3ab6a920d7","observation_id":"bc02a867-5ff0-4b0c-ab99-aa1ca6e2fd57","resolution":{"observed_at":"2026-08-09T22:50:03.105660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.092733Z","title":"Structural immaturity of human ipsc-derived cardiomyocytes: in silico investigation of effects on function and disease modeling","venue":null,"work_id":"eb6f5a58-14a3-4a3f-a518-816118e703f4","year":2018},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.340692Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:3931a2a2b38f7cc1069270ef8f027977bdbaac61861e4b372ea45754354678dd","observation_id":"0e93db18-3464-40eb-8b73-42702aa0e9a0","resolution":{"observed_at":"2026-08-09T22:50:03.096266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.081959Z","title":"A generalized laplacian of gaussian filter for blob detection and its applications","venue":null,"work_id":"c9b93021-60c9-41e7-99d4-deb3a7d6a9c2","year":2013},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.343960Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:223788182ba940c00fa5d8c22a1cd056bfc072d6533ac00a38b06f6f2760b8da","observation_id":"41765349-8207-47b5-a3c6-86e6b7759ee2","resolution":{"observed_at":"2026-08-09T22:50:03.085728Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.071325Z","title":"A guideline of selecting and reporting intraclass correlation coefficients for reliability research","venue":null,"work_id":"c9b02d00-aa9e-42e9-8266-15f5411cb7d1","year":2016},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.347549Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:7afc6a9d9bfe0c35b64e6fd7f6c50be9df3cab738076de39ff1e2153e9ce7e69","observation_id":"2b0561f8-aea3-46a5-889b-4a87ed8d8911","resolution":{"observed_at":"2026-08-09T22:50:03.074840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2405.17926","last_updated":"2024-10-28T08:37:53Z","snapshot_observed_at":"2026-08-16T13:48:44.499955Z","submitted_at":"2024-05-28T07:48:10Z","title":"SarcNet: A Novel AI-based Framework to Automatically Analyze and Score Sarcomere Organizations in Fluorescently Tagged hiPSC-CMs","version":2},"cited_work":{"arxiv_id":"2405.17926","doi":null,"metadata_source":"pith","pith_arxiv_id":"2405.17926","snapshot_observed_at":"2026-08-09T22:50:02.687957Z","title":"SarcNet: A Novel AI-based Framework to Automatically Analyze and Score Sarcomere Organizations in Fluorescently Tagged hiPSC-CMs","venue":"cs.CV","work_id":"07901c7e-6320-4cf5-84dc-60b6bea444ce","year":2024},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.350826Z"},"links":{"cited_paper":"/paper/2405.17926","citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:c6d51554f205e5cf9f8cb97820375ed28e896bb3b9486c4327dd02eceb9b46b5","observation_id":"870b783b-bec0-4df3-8f03-dad0cddece6d","resolution":{"observed_at":"2026-08-09T22:50:02.692400Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.060732Z","title":"Rater effects on essay scoring: A multilevel analysis of severity drift, central tendency, and rater experience","venue":null,"work_id":"c0a28d37-28c5-49c6-84a5-dadf03dbdaa6","year":2011},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.354719Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:0aba3c3dc958aa0c3a0d452e6c25ea69b42ed580f0c247725d3b4d8acbf522ef","observation_id":"4af80690-c786-4671-9337-38aa7a595ffb","resolution":{"observed_at":"2026-08-09T22:50:03.064729Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.050312Z","title":"A deep learning approach to estimate chemically-treated collagenous tissue nonlinear anisotropic stress-strain responses from microscopy images","venue":null,"work_id":"b23be0a3-9778-4f22-adfe-12871b9a28b0","year":2017},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.358120Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:e7830402a37722434bb30833165d35f4b0db5f642ab90fef4695bb6033f8a531","observation_id":"122b7940-ea6b-416b-b61b-45015a6b0f43","resolution":{"observed_at":"2026-08-09T22:50:03.054029Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.039999Z","title":"Human induced pluripotent stem cell for modeling cardiovascular diseases","venue":null,"work_id":"ccb593ae-1427-4400-981e-31e59ba2930f","year":2014},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.361569Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:85468ebed877ffde943b714615fae9b0426e4dbe65deef179595697b8f7b45ff","observation_id":"dee46289-225f-499a-8337-8f573a4cf976","resolution":{"observed_at":"2026-08-09T22:50:03.043714Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:03.028886Z","title":"Drug screening using a library of human induced pluripotent stem cell–derived cardiomyocytes reveals disease-specific patterns of cardiotoxicity","venue":null,"work_id":"35d84375-5439-40dd-924d-3a6ec4361e1e","year":2013},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.364906Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:8c0ab16743efdf3141d0f9c0869857db5176e8ee024a866fdff15d7a8d552736","observation_id":"3100af5f-ffae-45a8-889f-980485845847","resolution":{"observed_at":"2026-08-09T22:50:03.032896Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.368194Z","title":"A survey on deep learning in medical image analysis","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.368194Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:28586887df7b837b97fb10565dc110d3a82541ffaddf82b9aa05a1482a90be84","observation_id":"787e8d7e-2ff1-4ab1-81bd-f89587a5c3aa","resolution":{"observed_at":"2026-08-09T22:50:02.368194Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:50:02.371326Z","title":"Least squares quantization in pcm","venue":null,"work_id":null,"year":1982},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.371326Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:1173ceac9e4a20b26630093d0c01624b3390495de9cf4c6033cd08c3f61d8d9a","observation_id":"157b01e7-94fb-44d7-ba44-804bd02abc34","resolution":{"observed_at":"2026-08-09T22:50:02.371326Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:50:02.374651Z","title":"Lorensen and Harvey E","venue":null,"work_id":null,"year":1987},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.374651Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:9d1ba006143541eb3608fadfa8d80afb3c7e06803cc0c6ebc0e0efebbb08ef97","observation_id":"6e04cfb2-5293-4ef9-a3a0-de175a0d5bfd","resolution":{"observed_at":"2026-08-09T22:50:02.374651Z","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":"2026-08-09T22:50:03.005377Z","title":"The vertebrate muscle z-disc: sarcomere anchor for structure and signalling","venue":null,"work_id":"adfec3df-9fff-4c60-bee3-043fe6dcb596","year":2009},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.377925Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:c9b298b9c23f3ece05ace862eade44686b42150352d51c703a22a4a9bef8a94e","observation_id":"d3ea3677-ad65-4ae4-9261-4ac580bf1da7","resolution":{"observed_at":"2026-08-09T22:50:03.009196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.993444Z","title":"Theory of edge detection","venue":null,"work_id":"20d7d1a6-b2c4-4169-8d6f-daebc1d6316d","year":1980},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.381000Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:905fb9673682625b3d8dc5bd946c6ed66fbfff75d3dc64b13326e79bc7a81324","observation_id":"a8943896-6bbf-4741-b9f4-1a376528da4f","resolution":{"observed_at":"2026-08-09T22:50:02.997865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.982430Z","title":"2024 heart disease and stroke statistics: a report of us and global data from the american heart association","venue":null,"work_id":"3dd96e86-0d97-40e7-951c-2b9f58851cdd","year":2024},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.384143Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:23c837b8e8998bd30a2a0f48dae40176cac260d65fe958362262a1105e05886f","observation_id":"a10b000f-88e3-4333-aacf-a594dd10a091","resolution":{"observed_at":"2026-08-09T22:50:02.986414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.971008Z","title":"Transcriptome profiling of patient-specific human ipsc- cardiomyocytes predicts individual drug safety and efficacy responses in vitro","venue":null,"work_id":"5899de35-f66c-4ef2-9b3f-fbbee83ce731","year":2016},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.387507Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:85d603d1b820bfd4da96893e94dc216c7c064ab265819a264a5d36b49de5c853","observation_id":"800ad9b9-9b41-43f8-9f89-b58eed649f6c","resolution":{"observed_at":"2026-08-09T22:50:02.975520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.961034Z","title":"Sarcgraph: A python package for analyzing the contractile behavior of pluripotent stem cell-derived cardiomyocytes","venue":null,"work_id":"15a2bf38-dea2-450f-ab2a-6e39a207027d","year":2023},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.390798Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:ced750e7656e04dcfc40a2767e75500e9c0439252e6e057782af95ab03bac07f","observation_id":"ea786a6e-12e1-4d28-9119-9e951bba12ef","resolution":{"observed_at":"2026-08-09T22:50:02.964616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.951244Z","title":"Investigating deep learning model calibration for classification problems in mechanics","venue":null,"work_id":"2d789a1c-0f83-47cc-b3fa-548d28a5d72b","year":2023},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.393937Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:385019e774a467973b3ec621dd98bb6e0a35b91bb62b9eb07a4361760a7d1e82","observation_id":"55106c7f-59d2-4f9d-befe-a2e97ac9a84b","resolution":{"observed_at":"2026-08-09T22:50:02.954747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.940440Z","title":"Striated myocyte structural integrity: Automated analysis of sarcomeric z-discs","venue":null,"work_id":"ac9a38db-744e-440a-bfff-9ef1824630b0","year":2020},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.397322Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:bc810eaa34fd3c4db9e84e574dc04382ae7d8ab477f7086ac3cbc44db8411d81","observation_id":"5834c343-556e-4fbc-8957-27e44fc91c2f","resolution":{"observed_at":"2026-08-09T22:50:02.944254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.930216Z","title":"Explainable k-means and k-medians clustering","venue":null,"work_id":"c1ed1450-4396-4dda-ad59-b78cdf12f389","year":2020},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.401106Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:0d79334a87d66d1dd35b15b3692300f4b8c617be16d062d6964081e146ce77e7","observation_id":"67810c2b-25b4-4d02-9589-29b78b328db9","resolution":{"observed_at":"2026-08-09T22:50:02.933551Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.920178Z","title":"Algorithms for hierarchical clustering: an overview, ii.Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(6):e1219, 2017","venue":null,"work_id":"7573c7b6-e00a-4c8a-8139-3b6e673d2574","year":2017},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.404369Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:8d4302e647c06effb163200da707bc44733e253d6a215cf3f438f6edc137134f","observation_id":"f3108aca-50a4-4f71-a788-ec7da9c29b8c","resolution":{"observed_at":"2026-08-09T22:50:02.923719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.910502Z","title":"Derivation of human induced pluripotent stem cells for cardiovascular disease modeling","venue":null,"work_id":"f5890f5b-ddb6-4711-8936-29630e5077b2","year":2011},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.407441Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:fa09eb447a46e5bec6f185b8cc943651d4cfbc97b40ef7b62b06bcaa2ec4cc9d","observation_id":"ad3990e2-f4da-447e-b142-86e94992e524","resolution":{"observed_at":"2026-08-09T22:50:02.913964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.899311Z","title":"Independent regulation of z-lines and m-lines during sarcomere assembly in cardiac myocytes revealed by the automatic image analysis software sarcapp","venue":null,"work_id":"0dc9adbc-f051-444f-b12a-d6d9a64a0db7","year":2023},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.410404Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:03222f58221ae4a132855495e7886d7e2cd24ef4be10902ddacc74eaf02e54e4","observation_id":"6e778a00-edf9-4926-9275-60ac0b0e6968","resolution":{"observed_at":"2026-08-09T22:50:02.903691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.888449Z","title":"Sarcomere protein gene mutations in hypertrophic cardiomyopathy of the elderly.Circulation, 105(4):446–451, 2002","venue":null,"work_id":"48e32ac1-b9e2-4034-9237-6f973a71d163","year":2002},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.413424Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:8c53a42a6b6a2015733d2ed160fa7d9c750dc3852bb56f0c87c9cb1415de5b69","observation_id":"a8a3334b-927e-4d32-bebd-5a84847d32af","resolution":{"observed_at":"2026-08-09T22:50:02.892151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-17T13:03:40.359628Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-09T22:50:02.416520Z","title":"Dinov2: Learning robust visual features without supervision","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.416520Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:39c705da662b67ef9e20a09f0d2893244f98d629a5693d745eeec76bcdd0f8fa","observation_id":"ecb55910-7cb4-4b38-837b-a301244cdc1c","resolution":{"observed_at":"2026-08-09T22:50:02.416520Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T22:50:02.420076Z","title":"A threshold selection method from gray-level histograms","venue":null,"work_id":null,"year":1979},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.420076Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:5edd569e84610088e9e41349a0b1b1217e6e9b61d3f7fc8a90fedfaf22eadbb5","observation_id":"21efa139-783f-4596-959e-5a15a092f486","resolution":{"observed_at":"2026-08-09T22:50:02.420076Z","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":"2026-08-09T22:50:02.877778Z","title":"Tracking single hipsc-derived cardiomyocyte contractile function using contrax an efficient pipeline for traction force measurement","venue":null,"work_id":"47b981d0-b51f-41c9-8495-792c643ea72f","year":2024},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.423412Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:83b264ab97d362ecd10c70c324bd948dd6960853740dd17b8dbfd6b404a52a5d","observation_id":"e8261208-8a63-4bbd-b49b-7bfac62754f9","resolution":{"observed_at":"2026-08-09T22:50:02.881685Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.866711Z","title":"Sarcoptim for imagej: high-frequency online sarcomere length computing on stimulated cardiomyocytes.American Journal of Physiology-Cell Physiology, 311(2):C277–C283, 2016","venue":null,"work_id":"aacaa645-4522-45a0-bf35-cb77717a7e87","year":2016},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.426980Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:a40cb04546bffb59dadd57812d403b3cdaca92cb8c5bd6a8f6097b8bc66347f9","observation_id":"d8faa922-85a7-4b11-8be4-354ef01279e9","resolution":{"observed_at":"2026-08-09T22:50:02.870794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.855847Z","title":"Structural phenotyping of stem cell-derived cardiomyocytes","venue":null,"work_id":"2b767946-5932-4ef2-b39b-cdd152a2584c","year":2015},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.430207Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:5aa69c235ea07310d7f5a3ef0043d37e243d08f148e28bcd4ad807026aa45549","observation_id":"23469478-fd53-4bd9-81aa-a1a294d59675","resolution":{"observed_at":"2026-08-09T22:50:02.859543Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.844980Z","title":"Multi-imaging method to assay the contractile mechanical output of micropatterned human ipsc-derived cardiac myocytes","venue":null,"work_id":"c299054c-8cc6-45cd-91d8-f85a7b793865","year":2017},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.433389Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:b324fb963033f519eb57a39194d8bfdd5e1e270c6cca5dbbc02ffab45bb0edea","observation_id":"4ce05f87-da0e-4a67-ac8f-a9210cb7c03e","resolution":{"observed_at":"2026-08-09T22:50:02.849009Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.436619Z","title":"U-net: Convolutional networks for biomedical image segmentation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.436619Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:142c622626a8fd91883a8b58dfdc020d92a24aa4f7598e850f798f7cf6175a1d","observation_id":"324a60fd-0c7f-47a3-a936-1c7aaa1de643","resolution":{"observed_at":"2026-08-09T22:50:02.436619Z","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":"2026-08-09T22:50:02.827806Z","title":"Modeling cardiovascular diseases with hipsc-derived cardiomyocytes in 2d and 3d cultures","venue":null,"work_id":"7cbe83f6-7843-42a3-a976-87ae4442e93c","year":2020},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.439707Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:0b9378e9e00d376c4476a9dd01b45b727572198ada5dd5c7d5666ae755bd18d8","observation_id":"87b58bda-5571-4550-8ab6-cfad876ba7ca","resolution":{"observed_at":"2026-08-09T22:50:02.831624Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.816801Z","title":"A survey of decision tree classifier methodology","venue":null,"work_id":"9c56c1c0-1a92-447c-b361-65407c359c80","year":1991},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.443096Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:2676338c03230533cbd80ac8030c52ccc84659e698049d7d126f396dc3c2c13c","observation_id":"b06fecdf-bf30-49dd-9c85-2d32d45863a8","resolution":{"observed_at":"2026-08-09T22:50:02.821221Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.807245Z","title":"Quality metrics for stem cell-derived cardiac myocytes","venue":null,"work_id":"aa71bf45-7de2-429f-8e22-7ea5461d2e60","year":2014},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.446281Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:db2d7c8fd2870c8fba188c0bdaeda865edd6557103b96dfec53f6755c487c310","observation_id":"f911fc4e-5f60-4ca2-a756-4981bba112e9","resolution":{"observed_at":"2026-08-09T22:50:02.810635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.797612Z","title":"Induced pluripotent stem cell technology: a decade of progress","venue":null,"work_id":"4935d534-13e7-411e-9c35-b2c5ebf502d9","year":2017},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.449438Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:8590eaf13a0777c71abc86f55fabd21a9dd42871c03b13b2cac7b6cff0be36f3","observation_id":"ab77ffec-5c89-452a-aaad-416bcc72d7f8","resolution":{"observed_at":"2026-08-09T22:50:02.801099Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.787989Z","title":"Intraclass correlations: uses in assessing rater reliability","venue":null,"work_id":"2d9f32fd-8323-4b9a-ae16-540d66ef0b5d","year":1979},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.452890Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:2fd510ebca3a4fcc2d93c6d6c7e467090a044ad92242eac507ccf6bfa43397d5","observation_id":"9eea360e-959c-4558-8ff7-315fdcbf8b1f","resolution":{"observed_at":"2026-08-09T22:50:02.791526Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.778354Z","title":"Harding, Neda Mohammadi, Valeria V Orlova, Milena Bellin, Christine L Mummery, and Berend J van Meer","venue":null,"work_id":"d7ecdbd5-43db-42fe-864c-6221a5369e23","year":2022},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.456041Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:e21c2664c96599ef240258e70cdde1ecf113adf1eab1cf57a92cb924670143d3","observation_id":"ac97b7e1-0adb-4896-9ecd-20976860ca33","resolution":{"observed_at":"2026-08-09T22:50:02.781802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.768553Z","title":"High content analysis identifies unique morphological features of reprogrammed cardiomyocytes","venue":null,"work_id":"c2c64c0f-2af8-4622-be7a-eed7d0b1456c","year":2018},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.459154Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:f4b672a3f22422678afd06f7fa5f8b7b8513d876a9d435f45810b8ae5821ce6d","observation_id":"fab58a7c-9171-411e-abf5-57de87a83a41","resolution":{"observed_at":"2026-08-09T22:50:02.772056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.757834Z","title":"Efficientnetv2: Smaller models and faster training","venue":null,"work_id":"9e0cf866-f8be-4951-95f6-f65222e08f0b","year":2021},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.462294Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:db2e6a944644def53079d3b65c9af1c1ceff50e8feaf3b04892cf415ce8855fa","observation_id":"02175a73-6628-4b4e-9484-c40dbad2b550","resolution":{"observed_at":"2026-08-09T22:50:02.761411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1161/circresaha.118.314505","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Toepfer, Arun Sharma, Marcelo Cicconet, Amanda C","venue":"Circulation Research","work_id":"770a5e2c-0c7d-44d6-8ba9-a71281830fa2","year":2019},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.465329Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:0d8e68abaacf6ce7fe3af7aad5d69314027596638bde96258c08ea91c9450a2a","observation_id":"4bb4e069-b161-412d-81ab-22af7a065f98","resolution":{"observed_at":"2026-08-09T22:50:02.519588Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.468762Z","title":"Schönberger, Juan Nunez-Iglesias, François Boulogne, Joshua D","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.468762Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:dd78418c8439d23a6efbad51aec1003975bcecc5876f2ca823fc1f14c7bbeb3d","observation_id":"33ad987c-a1b5-49bb-b28e-da6d20a686bc","resolution":{"observed_at":"2026-08-09T22:50:02.468762Z","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":"2026-08-09T22:50:02.746904Z","title":"A survey on semi-supervised learning","venue":null,"work_id":"3eea18e5-bc0b-4223-84c7-4e9625f12b9b","year":2020},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.471940Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:97e2a3752abe980effdeef7174ee3efbeff155b8814289a1dce63d3062f13d78","observation_id":"b52cfbcb-ba7f-4dd8-81aa-0b11452beb6f","resolution":{"observed_at":"2026-08-09T22:50:02.751054Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.736364Z","title":"Modelling inherited cardiac disease using human induced pluripotent stem cell-derived cardiomyocytes: progress, pitfalls, and potential","venue":null,"work_id":"5610f9c3-c16d-48f9-bd3a-a2f7b7ce70ce","year":2018},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.474988Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:3a84f8c4273d62fe47be8f8c551992c71f03d0138a5c174152e4a220f2150eb9","observation_id":"9d152ae1-ff95-464e-9ffb-d70639532bf4","resolution":{"observed_at":"2026-08-09T22:50:02.740300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.726212Z","title":"S4l: Self-supervised semi-supervised learning","venue":null,"work_id":"071923b3-110b-4c7b-9d1e-f14796905f27","year":2019},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.478549Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:098bcdc8bcc8e82e0dd422164f92eb8515f6fdadf1ba626be8151a107d140155","observation_id":"6cc47a81-f6fb-429d-a672-0a0b503d7c94","resolution":{"observed_at":"2026-08-09T22:50:02.729810Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+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":"2026-08-09T22:50:02.716115Z","title":"Sarc-graph: Automated segmentation, tracking, and analysis of sarcomeres in hipsc-derived cardiomyocytes","venue":null,"work_id":"097b8ed5-fad5-4cf6-984b-32dd892c30f3","year":2021},"citing_paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-09T22:50:02.481584Z"},"links":{"citing_paper":"/paper/2501.18714"},"observation_digest":"sha256:871716e969a3ade4be16c56eb9a16feb57020455559591f8c167aa09ee29ce1c","observation_id":"1ec2c0d1-cc3d-4626-a413-a965cd0e4954","resolution":{"observed_at":"2026-08-09T22:50:02.719750Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.18714","last_updated":"2025-01-30T19:22:54Z","latest_version":1,"primary_category":"q-bio.QM","snapshot_observed_at":"2026-08-14T16:09:50.454837Z","submitted_at":"2025-01-30T19:22:54Z","title":"Quantifying HiPSC-CM Structural Organization at Scale with Deep Learning-Enhanced SarcGraph"},"reference_resolution":{"displayed":69,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":9,"verified_exact":3,"verified_fuzzy":57},"total_outbound_references":69},"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":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 22 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 0 inbound Pith citation observations for arXiv:2501.18714."}