{"as_of":"2026-08-13T14:30:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:ef08dc7b25e816636ca83927e7310e87c4a5f938269c78e08251bb5b26a7fb22","coverage":[{"denominator":25,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":25,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T17:24:16.902455Z","state":"measured"},{"denominator":25,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":25,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-13T06:32:02.005865+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/2411.12636/citation-record","integrity":"/paper/2411.12636/integrity","json":"/paper/2411.12636/citation-record.json","paper":"/paper/2411.12636"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.26443/seismica.v2i1.368","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:24:17.034033Z","title":"Noda, S., Yamamoto, S., Sato, S., Iwata, N., Korenaga, M., Ashiya, K.,","venue":null,"work_id":"0cf685b2-f6ec-42e4-a10e-73004f36215b","year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.846853Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:3601ff12678cb00af3e3e47f99056d88361a4df9e3c1ce1b15205631a1c968c9","observation_id":"495424ca-e7f7-494c-825c-db47225a497d","resolution":{"observed_at":"2026-08-12T17:24:17.038306Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T17:24:16.829731Z","title":"Mohr, F., van Rijn, J.N.,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.829731Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:fc56f452ddd65bf65656c7fbeef1a5db940bde22ff76ebf1f85972260c5ad164","observation_id":"ef073d13-6058-447c-90f7-2ce8181cca52","resolution":{"observed_at":"2026-08-12T17:24:16.829731Z","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":"10.5194/essd-2023-470","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:24:17.099440Z","title":"Louboutin, M., Lange, M., Luporini, F., Kukreja, N., Witte, P.A., Herrmann, F.J., Velesko, P., Gorman, G.J.,","venue":null,"work_id":"52233875-81c6-42b2-a660-45189b4a5494","year":2023},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.813793Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:102abaf4ddd43b63b4c00dfcf4d817854758175f11aaf5278233fe10a0b7858c","observation_id":"1d5de874-c450-43ef-8b9f-3d9d6a4d98dd","resolution":{"observed_at":"2026-08-12T17:24:17.103896Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1186/s43074-025-00160-z","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:24:16.976985Z","title":"18 Singh, S.P., Silwal, V.,","venue":null,"work_id":"907e4bc8-4646-4079-ab35-d6f96b859ff8","year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.883021Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:b708c5210d589ac0d3ff7a3d6ddb18ff5ffa228d0fb76603419548c8ea509edb","observation_id":"d35b9814-5738-40a5-8a47-be2ff4407d0c","resolution":{"observed_at":"2026-08-12T17:24:16.980873Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2201.12150","last_updated":"2025-01-28T14:39:26Z","snapshot_observed_at":"2026-08-11T12:27:08.133516Z","submitted_at":"2022-01-28T14:34:32Z","title":"Learning Curves for Decision Making in Supervised Machine Learning: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.12150","snapshot_observed_at":"2026-08-12T17:24:16.833684Z","title":"CoRR abs/2201.12150","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.833684Z"},"links":{"cited_paper":"/paper/2201.12150","citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:1cc05727521d95f308672473275fa819fe6eb9c8c43297a6331dedfd1d1a5459","observation_id":"1d25cdf3-7840-4444-8d52-f3b2c915a3b3","resolution":{"observed_at":"2026-08-12T17:24:16.833684Z","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":"2026-08-12T17:24:16.838613Z","title":"Mousavi, S.M., Beroza, G.C.,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.838613Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:4d4c0c73c27b0ff41a0bd48ff4cff6641547f3d62bfbc304937afc8f5bacae14","observation_id":"da725e31-04e5-4a04-9c36-3371c3b438f2","resolution":{"observed_at":"2026-08-12T17:24:16.838613Z","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":"2026-08-12T17:24:16.817752Z","title":"Luporini, F., Louboutin, M., Lange, M., Kukreja, N., Witte, P., H¨ uckelheim, J., Yount, C., Kelly, P.H.J., Herrmann, F.J., Gorman, G.J.,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.817752Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:75526a72c36c15355ba2545c2c6fa070cabff391029edf2171fd01dd8ac7f5fb","observation_id":"e7232a34-d848-4b64-a4b1-c11cca1b4a25","resolution":{"observed_at":"2026-08-12T17:24:16.817752Z","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":"2026-08-12T17:24:16.806025Z","title":"Demanet, L.,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.806025Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:dc11a8dc0f35ff9af4198b46ea76e67436a4925907e7839ff044386fc1a71efe","observation_id":"4c4fa39c-47ea-4ec4-acab-86c426a41aec","resolution":{"observed_at":"2026-08-12T17:24:16.806025Z","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":"10.1126/sciadv.1700578","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:24:17.005243Z","title":"Richardson, A.,","venue":null,"work_id":"70c01a64-90f0-4b1a-bdde-91ec19307898","year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.859150Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:fbca66fe439cb5c1113bbdba086d05731c58abd812047e4ad3c0634ba294a6df","observation_id":"5982ee5e-3a85-4222-93bd-a07084e622ae","resolution":{"observed_at":"2026-08-12T17:24:17.010356Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T17:24:17.350121Z","title":"Earth Science Informatics","venue":null,"work_id":"040aacae-0d9b-4cd2-890a-45e874add7b4","year":2017},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.867535Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:3fef85410ec7621f55e64f94ca89a7d3121dc08ac659bc525640eb921eec705d","observation_id":"b86eae87-ac58-4eef-9ce7-1f906ae6a9c0","resolution":{"observed_at":"2026-08-12T17:24:17.356079Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T17:24:16.871904Z","title":"Ross, Z., Meier, M.A., Hauksson, E.,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.871904Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:680b847fd34f740e67944e8e1b3eee8573a99620a835176b2d91104203dccd8c","observation_id":"77b99a49-df1a-4b91-9610-0461f42b0e7a","resolution":{"observed_at":"2026-08-12T17:24:16.871904Z","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":"2026-08-12T17:24:16.879215Z","title":"IEEE Geoscience and Remote Sensing Letters 1, 1–1","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.879215Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:7d7aee328f03e176310f5535682a8953dca6a706369d69670763a8437578753b","observation_id":"6ce39923-d5ba-40a1-9014-3ec8b234ce4a","resolution":{"observed_at":"2026-08-12T17:24:16.879215Z","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":"2026-08-12T17:24:16.886769Z","title":"2023.10.002","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.886769Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:d4b607e11f43c80e66909ee0b3f30961ea49856604bf5e0d56b3e37ee10c9c91","observation_id":"988d0f78-ca25-4ced-9582-89dace30a27c","resolution":{"observed_at":"2026-08-12T17:24:16.886769Z","resolver_source":null,"status":"malformed_identifier"},"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":"2026-08-12T17:24:16.894320Z","title":"Seismological Research Let- ters 93, 1695–1709","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.894320Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:99d9af946295dd70e55d3066dadcbfb14c1f26eddfa72f630ec280511f414eb0","observation_id":"29d8dfee-d9c3-4f99-8b81-7cfb68104125","resolution":{"observed_at":"2026-08-12T17:24:16.894320Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.00987","last_updated":"2022-10-03T14:53:17Z","snapshot_observed_at":"2026-08-13T14:14:03.789749Z","submitted_at":"2022-10-03T14:53:17Z","title":"Data Budgeting for Machine Learning","version":1},"cited_work":{"arxiv_id":"2210.00987","doi":"10.48550/arxiv.2210.00987","metadata_source":"pith","pith_arxiv_id":"2210.00987","snapshot_observed_at":"2026-08-12T18:16:22.223912Z","title":"Data Budgeting for Machine Learning","venue":"cs.LG","work_id":"bf82b190-cb89-4bf9-bf61-0eba9071f6e0","year":2022},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.898241Z"},"links":{"cited_paper":"/paper/2210.00987","citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:c3d0ca4f42035e55fc243a169f201f3cbb681b7532d3673a205bb3258fdba98e","observation_id":"2deb2185-5570-4b91-8eb9-ae96f4b7e1bd","resolution":{"observed_at":"2026-08-12T17:24:16.937173Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+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":"2026-08-12T17:24:16.821861Z","title":"maintainers, T., contributors,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.821861Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:a7237db2d496b38fcd04482a1734b2e5740d4fee29d4be5ee87e8b3c9466e230","observation_id":"92a82dc0-463c-444b-9432-8b7d196ad968","resolution":{"observed_at":"2026-08-12T17:24:16.821861Z","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":"2026-08-12T17:24:16.902455Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.902455Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:382218ea35b6f2569bdca35e83d58f4176ff03a75e67f829c2ddb7e44a9f73b4","observation_id":"fc1f0d72-53c9-4250-b446-05671ae33878","resolution":{"observed_at":"2026-08-12T17:24:16.902455Z","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":"2026-08-12T17:24:16.875413Z","title":"Saad, H., M","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":123,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.875413Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:f9e2d099ab4e68aa04c12525ab8eb5ab8097c5bf6c50513d632540ee31ce9ae5","observation_id":"079c3d8b-980b-4182-9d2d-487304be0f68","resolution":{"observed_at":"2026-08-12T17:24:16.875413Z","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":"2026-08-12T17:24:16.809863Z","title":"Geurts, P., Ernst, D., Wehenkel, L.,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":218,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.809863Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:cb92f7b361bcb908f7896ebc86fbfcaa85ac8bca98f892aee65a85792a6760dd","observation_id":"573521e8-fd3b-46d3-bfce-cc3578034a10","resolution":{"observed_at":"2026-08-12T17:24:16.809863Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1190/1.1437051","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:24:16.955059Z","title":"The Leading Edge 13, 927–936","venue":null,"work_id":"139f2ebf-2ba7-4e26-bbf6-e820e763c508","year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":1994,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.890356Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:6cf6e769394d91b8058214fd4176302dff802704fbd6cc15541a2b1959d234a5","observation_id":"70cb7a34-bdff-4356-a8bb-62ee119f9858","resolution":{"observed_at":"2026-08-12T17:24:16.959783Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5047/eps.2011.10.005","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:24:17.020785Z","title":"Earth Planets and Space 64, 305–308","venue":null,"work_id":"f567f827-90e0-4883-bc63-ccc83d785078","year":2011},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":2012,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.851052Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:7c85ea9ecc1c385f24c566ec4d237274ea503951dca6d72683aec42a35077edb","observation_id":"8c2ef476-62ca-47cb-8820-dee14edcd254","resolution":{"observed_at":"2026-08-12T17:24:17.024926Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1801.07232","last_updated":"2018-01-31T17:24:56Z","snapshot_observed_at":"2026-08-10T08:54:56.553908Z","submitted_at":"2018-01-22T18:27:19Z","title":"Seismic Full-Waveform Inversion Using Deep Learning Tools and Techniques","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1801.07232","snapshot_observed_at":"2026-08-12T17:24:16.863149Z","title":"URL: https://arxiv.org/abs/1801.07232, arXiv:1801.07232","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":2018,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.863149Z"},"links":{"cited_paper":"/paper/1801.07232","citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:546ebb355434bd6fd3886cf4f00f75f5445462587fd02df640b1171792afe4f8","observation_id":"e83c159e-3aeb-440b-a936-48b144353fde","resolution":{"observed_at":"2026-08-12T17:24:16.863149Z","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":"2026-08-12T17:24:16.826072Z","title":"Interpretation 10, SE31– SE39","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.826072Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:90b89ca60fc6446a29f3b99a8c8d5ba5b4ee9bfc1083f7ee447ecdfde40bd4b4","observation_id":"906c12af-6907-4b4c-b832-77055011c0e4","resolution":{"observed_at":"2026-08-12T17:24:16.826072Z","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":"10.1146/annurev-earth-071822-100323","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T17:24:17.046649Z","title":"Annual Review of Earth and Planetary Sciences 51, 105–129","venue":null,"work_id":"8d631e50-e7a9-415d-86c6-fff32710a16a","year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.842667Z"},"links":{"citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:21f690a6c0755520c6689ff1eeb115c6505d40f11a0ad9aebae286c4ffd59601","observation_id":"8ef37283-ca02-4982-8d47-5cc8a09be230","resolution":{"observed_at":"2026-08-12T17:24:17.051068Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.18116","last_updated":"2024-03-26T21:45:29Z","snapshot_observed_at":"2026-08-13T00:44:18.861904Z","submitted_at":"2024-03-26T21:45:29Z","title":"QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.18116","snapshot_observed_at":"2026-08-12T17:24:16.800960Z","title":"URL: https://arxiv.org/abs/2403.18116, arXiv:2403.18116","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation","version":2},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-12T17:24:16.800960Z"},"links":{"cited_paper":"/paper/2403.18116","citing_paper":"/paper/2411.12636"},"observation_digest":"sha256:54d3e095f93ce88778bc8000365b38890a186e5a834fc0c895a86fb30b46e8d0","observation_id":"35010915-b2e5-4553-8c3e-0795386d21db","resolution":{"observed_at":"2026-08-12T17:24:16.800960Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2411.12636","last_updated":"2025-04-29T10:04:50Z","latest_version":2,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-12T17:17:14.070292Z","submitted_at":"2024-11-19T16:49:58Z","title":"PyAWD: A Library for Generating Large Synthetic Datasets of Acoustic Wave Propagation"},"reference_resolution":{"displayed":25,"state_counts":{"malformed_identifier":2,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":14,"verified_exact":7,"verified_fuzzy":1},"total_outbound_references":25},"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":"2026-08-13T06:32:02.005865+00:00","source":"crossref"},{"observed_at":"2026-08-13T06:31:53.387327+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2411.12636."}