{"as_of":"2026-08-22T20:41:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e35a352cb08832ae085e6235dcf02f9656a0db070a8e3d5ed7c718f5bc757828","coverage":[{"denominator":32,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":32,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T22:54:59.294142Z","state":"measured"},{"denominator":32,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":32,"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/2506.20359/citation-record","integrity":"/paper/2506.20359/integrity","json":"/paper/2506.20359/citation-record.json","paper":"/paper/2506.20359"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1002/sam.11287","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Trajectory analysis: An overview,","venue":"Statistical Analysis and Data Mining The ASA Data Science Journal","work_id":"4665ce44-4db8-4b0a-af9d-dd59c36e8a1f","year":2015},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:57.117666Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:6fb00a095ffde6adbdc391d4bde4f586362030d3ae4d448aac3e6ba997cf7c8a","observation_id":"71458b19-6525-4a45-8ea2-18b196a5df2f","resolution":{"observed_at":"2026-08-06T22:54:59.657199Z","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":"2026-08-06T22:54:57.299263Z","title":"Ptrail — a python package for parallel trajectory data preprocessing,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:57.299263Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:2b24a170aca3434e7b593db1ae42f9c3000d70b2a68b262733a5ee65a61a92b0","observation_id":"6927683d-007d-41f8-b461-23450be342ef","resolution":{"observed_at":"2026-08-06T22:54:57.299263Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.20174","last_updated":"2025-04-28T18:16:57Z","snapshot_observed_at":"2026-08-16T11:18:25.555729Z","submitted_at":"2025-04-28T18:16:57Z","title":"A Novel Multilevel Taxonomical Approach for Describing High-Dimensional Unlabeled Movement Data","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.20174","snapshot_observed_at":"2026-08-06T22:54:57.420777Z","title":"A novel multilevel taxonomical approach for describing high-dimensional unlabeled movement data,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:57.420777Z"},"links":{"cited_paper":"/paper/2504.20174","citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:df2e650b9adff37f48635b3d20705cad6c18a3a8961bcc1fe9825f4fbd778d72","observation_id":"0193de1e-015b-4c7d-a9d1-4c094543a3bc","resolution":{"observed_at":"2026-08-06T22:54:57.420777Z","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":"2026-08-06T22:55:01.588965Z","title":"A review of feature selection methods based on meta-heuristic algorithms,","venue":null,"work_id":"4d44647a-4318-4316-bfce-4d7ba33e4cb6","year":2025},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:57.537044Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:72b5295000ad763f40651b2d8e0a462d86b58a0297d3c58d5672ee00d6f74092","observation_id":"cdf71994-8c3a-4918-b035-f90a94a051d6","resolution":{"observed_at":"2026-08-06T22:55:01.592974Z","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":"2026-08-06T22:54:57.622888Z","title":"Feature selection in machine learning: Methods and comparison,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:57.622888Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:ba51f48de674726bf987ed86d845afa71a5d268643f4660cc83f07cda3a52389","observation_id":"5d25f7f1-0862-4bb8-9cdb-03fd6c8efdce","resolution":{"observed_at":"2026-08-06T22:54:57.622888Z","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":"2026-08-06T22:55:01.579013Z","title":"A survey on big data for trajectory analytics,","venue":null,"work_id":"5fcfed5b-5e05-47af-b2c4-a4d1c31ad9ec","year":2020},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:57.700875Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:c23192bf6810965b03ceba8fc3462396776e0c30ab78f130e317c7c00ea4aa67","observation_id":"927244c2-99f8-4ef3-a1fe-025d5d2eaad8","resolution":{"observed_at":"2026-08-06T22:55:01.582926Z","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":"2026-08-06T22:54:57.769850Z","title":"Enhancing global mar- itime traffic network forecasting with gravity-inspired deep learning models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:57.769850Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:8ac4371a4a7954dc36546bd83628b12041557ce3fd8972e91473ebdf1c92040a","observation_id":"2e411f5b-3776-4c3e-b419-19caf3a983f3","resolution":{"observed_at":"2026-08-06T22:54:57.769850Z","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":"2026-08-06T22:54:57.821698Z","title":"Multi-path long-term vessel trajectories forecasting with probabilistic feature fusion for problem shifting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:57.821698Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:3505ccbc130b237c6ae4057bdfe6c2d1c8ff42003a5fb350e4a62dcb3629b6f5","observation_id":"4acc515a-b055-4f76-9c51-985ce4bf8b4e","resolution":{"observed_at":"2026-08-06T22:54:57.821698Z","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":"2026-08-06T22:55:01.559621Z","title":"A semi-supervised method- ology for fishing activity detection using the geometry behind the trajectory of mul- tiple vessels,","venue":null,"work_id":"9a5729f4-eadc-43bc-b425-058a5e6404cc","year":2022},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:57.878855Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:b3c987f8d9d4159e2b7ff77181ec433f5fea0b9d8565ac2becdc356a2e5c0b00","observation_id":"59c64cee-4240-471c-98d2-f742a98dc29f","resolution":{"observed_at":"2026-08-06T22:55:01.563288Z","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":"2026-08-06T22:55:01.549258Z","title":"A study on the geometric and kine- matic descriptors of trajectories in the classification of ship types,","venue":null,"work_id":"3b3fa5b8-f23a-4e71-9102-4b6a1989b540","year":2022},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:57.958334Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:59b290ab8478dc2fede61e0e9a4ab1573fdd688f931b663c941e7518804e97a4","observation_id":"d1cbca3a-bd53-4887-9f2d-dd389db84326","resolution":{"observed_at":"2026-08-06T22:55:01.552861Z","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":"2026-08-06T22:55:01.539284Z","title":"Assessing com- pression algorithms to improve the efficiency of clustering analysis on ais vessel trajectories,","venue":null,"work_id":"80a5a28b-cec5-44c7-8675-72fe69a72b8d","year":2023},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.042652Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:46c5dc64817f49ffe47d2659ce518345ebb7adcdf461ac8c7352c6a42717734d","observation_id":"1bb4fc0f-435a-4840-992c-cd6369cd3967","resolution":{"observed_at":"2026-08-06T22:55:01.543304Z","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":"2026-08-06T22:55:01.530419Z","title":"Uncovering vessel movement patterns from ais data with graph evolution analysis,","venue":null,"work_id":"30f29e49-bebd-4d3b-ba10-b3f235b9163a","year":2020},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.100335Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:44c0b158a85860f59a7f0722d3668209792fa2938bed946e9e46f7459f37dc58","observation_id":"5e3397e5-1265-433a-87de-054793b18f1f","resolution":{"observed_at":"2026-08-06T22:55:01.533803Z","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":"2026-08-06T22:55:01.521904Z","title":"Un- derstanding evolution of maritime networks from automatic identification system data,","venue":null,"work_id":"60eb4f47-c2ef-4949-af0a-f7bb6a78ce20","year":null},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.135674Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:f4e0dbf24ebe6fe361f25353e89cf3174c45c4888161f276ac8da004b8cc68e3","observation_id":"dad55a6c-28f1-46ca-afa4-db5f91ef6ebc","resolution":{"observed_at":"2026-08-06T22:55:01.525092Z","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":"2026-08-06T22:54:58.192599Z","title":"Challenges in vessel behavior and anomaly detection: From classical machine learning to deep learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.192599Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:21b86d46c51dc5a842f76964ce765e92efc926a9a23d2846d52689f4ba619a6d","observation_id":"8ac69a70-b922-49b0-846d-81ca5026b816","resolution":{"observed_at":"2026-08-06T22:54:58.192599Z","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":"2026-08-06T22:54:58.238190Z","title":"A trajectory scoring tool for local anomaly detection in maritime traffic using visual analytics,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.238190Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:d1b2b9c7cdd05ca91bb7103c302c0631a635364be2433180283c81e9067a3f97","observation_id":"dd2cbee7-5d1a-4f08-ba58-3c2af49d8b08","resolution":{"observed_at":"2026-08-06T22:54:58.238190Z","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":"2026-08-06T22:55:01.503855Z","title":"A dash- board tool for mobility data mining preprocessing tasks,","venue":null,"work_id":"416f00d4-aa03-4442-85c5-cb529b23b128","year":2022},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.306112Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:db248ccea273ac8a7515ea5fce9429f7ea0ca98d68cb40176f7a8d153c802fdf","observation_id":"e9622ce8-5176-4d33-be53-4cb229129c11","resolution":{"observed_at":"2026-08-06T22:55:01.507235Z","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":"7548.35075","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:55:00.106529Z","title":"Vessel pattern recognition using trajectory shape feature,","venue":null,"work_id":"1cff556f-dc1d-41a2-9169-89d81f241feb","year":2021},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.377041Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:0f7cf5595159d79b02a27526e75517bd594beb1ed017d059ff1c8c5550630c84","observation_id":"f4bc51d0-a29e-4ce4-8df1-474ebcd4bd21","resolution":{"observed_at":"2026-08-06T22:55:00.184849Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"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":"1906.04928","last_updated":"2019-06-24T08:17:06Z","snapshot_observed_at":"2026-08-15T18:46:22.206765Z","submitted_at":"2019-06-11T13:43:54Z","title":"Deep Learning for Spatio-Temporal Data Mining: A Survey","version":2},"cited_work":{"arxiv_id":"1906.04928","doi":"10.48550/arxiv.1906.04928","metadata_source":"pith","pith_arxiv_id":"1906.04928","snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":"Deep Learning for Spatio-Temporal Data Mining: A Survey","venue":"cs.LG","work_id":"ebda0248-0c6c-47d8-8b5f-ed2fdabf3aab","year":2019},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.440541Z"},"links":{"cited_paper":"/paper/1906.04928","citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:e657c9bcaa4bc37d4502b12481f97f35968ba25ba2140ab805e6f2a6a1d755bb","observation_id":"af9ff887-9cd7-4e82-8e92-934026bacde9","resolution":{"observed_at":"2026-08-06T22:54:59.525732Z","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":"4717.34839","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:59.935334Z","title":"Fréchet kernel for trajectory data analysis,","venue":null,"work_id":"af3ba18f-4e55-48f5-a99f-5ad2296e8728","year":2021},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.509630Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:134dac582a3ce21a11cd8390986ff500f60a11a66df6ada6a6f84b5ee2b3f07b","observation_id":"812cf0d5-9fde-4c74-b97f-b93d4e06699c","resolution":{"observed_at":"2026-08-06T22:55:00.007226Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"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":"2026-08-06T22:55:01.494599Z","title":"Forward-backward selection with early dropping,","venue":null,"work_id":"d9ac6e06-df94-48eb-91ea-c92d87aad70a","year":2019},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.591461Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:d6a01e1e81d73ce29dbc6c6376c03b0acedd22bca8a4cf0bf3d331689c917aba","observation_id":"5ae5cf28-568a-46d8-afa9-b38688f3e3ff","resolution":{"observed_at":"2026-08-06T22:55:01.498288Z","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":"2026-08-06T22:55:01.486000Z","title":"A feature selection method for multi-dimension time-series data,","venue":null,"work_id":"cb3bc26f-8296-47a6-ac71-0542ff55330a","year":2021},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.656234Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:5ab302e405c589c17074a9775604e978dfd1ccb92431e9dfca79a2e920179a77","observation_id":"e3a00d0d-38bd-4e6a-8f93-5e4f7aa7778f","resolution":{"observed_at":"2026-08-06T22:55:01.489094Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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":"2026-08-06T22:55:01.476420Z","title":"An introduction to variable and feature selection,","venue":null,"work_id":"4c1c1323-ba3f-4b6e-9637-c8f15da1ccd5","year":2003},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.705550Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:f29e3d8c2268c2983c0b770aedbdf505d40de3efb365f9ef753ddfa01b73c075","observation_id":"814a0a11-5374-468f-943f-07a887551930","resolution":{"observed_at":"2026-08-06T22:55:01.480433Z","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":"2026-08-06T22:55:01.467402Z","title":"Evolving feature selection: Syner- gistic backward and forward deletion method utilizing global feature importance,","venue":null,"work_id":"bf719261-dc87-4d34-bc04-973ac47a4a9f","year":2024},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.774811Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:ec624a01b8bad7c470418f4d5ecb0c33f19db292bd51139a8eeb733811423114","observation_id":"ffd03025-9813-40cd-b99e-03b922142ee5","resolution":{"observed_at":"2026-08-06T22:55:01.470683Z","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":"2026-08-06T22:55:01.372366Z","title":"Multi-dimensional feature selection and com- bination method of aerospace target based on k-means clustering and information entropy,","venue":null,"work_id":"87e8d7de-2941-4cb3-8605-c7387a8f4ab1","year":2023},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.852181Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:3e2ccf384c423f9ddc6c2c01223d27730d9defadeaac63ddfe0f64aa8e1fbee5","observation_id":"cf4d1c35-170c-48a1-b694-6376ee193edd","resolution":{"observed_at":"2026-08-06T22:55:01.459416Z","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":"1904.02301","last_updated":"2019-04-04T01:36:04Z","snapshot_observed_at":"2026-08-20T11:16:43.606346Z","submitted_at":"2019-04-04T01:36:04Z","title":"Cost-Sensitive Feature Selection by Optimizing F-Measures","version":1},"cited_work":{"arxiv_id":"1904.02301","doi":null,"metadata_source":"pith","pith_arxiv_id":"1904.02301","snapshot_observed_at":"2026-08-06T22:54:59.764673Z","title":"Cost-Sensitive Feature Selection by Optimizing F-Measures","venue":"cs.CV","work_id":"38145fd3-82c9-4064-9b4d-3fa36c680b03","year":2019},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.888785Z"},"links":{"cited_paper":"/paper/1904.02301","citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:2c74d6703252e264ee7ee9d9946a0f53de80a0bd1716bb2923537a49dd8afde8","observation_id":"0dce6d55-4f4a-4261-9f31-d2cfd443155e","resolution":{"observed_at":"2026-08-06T22:54:59.825026Z","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":"2026-08-06T22:55:01.025717Z","title":"Data from: Movement tactics of a mobile predator in a meta-ecosystem with fluctuating resources: the arctic fox in the high arctic,","venue":null,"work_id":"f1ba8de5-49cf-494d-a88a-1b5e5c035edf","year":2016},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:58.927204Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:6b9eb0e608b3d6ec371dc6de9f3d8da2c001b8a48ac02b2a80465769b4c860bd","observation_id":"a709a3ce-02c6-4e74-b1ff-49c963a1e854","resolution":{"observed_at":"2026-08-06T22:55:01.189521Z","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":"2026-08-06T22:55:00.750645Z","title":"Ais ship type codes reference,","venue":null,"work_id":"fbf88732-40e1-476c-9f73-86f5d8156c17","year":2025},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:59.013105Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:4fb09827f422f8e994fdeeda4a9a222df50e65ce2b23b895c31718814051c060","observation_id":"56fc00ac-67e5-4910-9d4c-9655ba864d39","resolution":{"observed_at":"2026-08-06T22:55:00.914197Z","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":"2026-08-06T22:55:00.565622Z","title":"Interna- tional best track archive for climate stewardship (ibtracs) project, version 4,","venue":null,"work_id":"0afef22a-1227-4947-a045-ecb5815f91c0","year":2018},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:59.078186Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:5b589a69ca8d774e6376c57e78b2177d381109ee19af67c36b2151c907456820","observation_id":"b8346813-e36c-40b2-acf0-6ae8d49ed273","resolution":{"observed_at":"2026-08-06T22:55:00.642867Z","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":"10.1016/j.inffus.2022.01.011","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T22:54:59.353039Z","title":"Tabular data: Deep learning is not all you need,","venue":null,"work_id":"7d0f1896-13a7-4c7d-a048-9b70741820f2","year":2022},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:59.115107Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:91adf9ab8536ba0bbb3424fd5be168c989619071aa7c45bf152f266f23e8af26","observation_id":"af17ae5b-b809-486d-b154-c80afad1f24d","resolution":{"observed_at":"2026-08-06T22:54:59.396104Z","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":"2026-08-06T22:54:59.176786Z","title":"Random forests,","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:59.176786Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:8c279734519a07ca2d4930c5166cbbbfc97e0f7bddc844dfdf60af48596fdaac","observation_id":"1d73f96b-7bc4-4cbc-aa21-d8434bff1f83","resolution":{"observed_at":"2026-08-06T22:54:59.176786Z","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":"2026-08-06T22:54:59.257104Z","title":"Xgboost: A scalable tree boosting system,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:59.257104Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:017121ec06942621907f9f36c3888bb948e72afba0503d9cdbd888f1cf05086b","observation_id":"62b76ef1-264e-4e73-be05-58db690dae70","resolution":{"observed_at":"2026-08-06T22:54:59.257104Z","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":"2026-08-06T22:55:00.445827Z","title":"Learning representations by back-propagating errors,","venue":null,"work_id":"53935d77-8704-4abd-bd45-3ad1f3746d51","year":1986},"citing_paper":{"arxiv_id":"2506.20359","last_updated":"2025-06-25T12:21:20Z","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T22:54:59.294142Z"},"links":{"citing_paper":"/paper/2506.20359"},"observation_digest":"sha256:e96d29c12921e2cf5891af3462de8612c513be50b6000a2d70f20150a3fe4a9c","observation_id":"4bcd46c8-a1dc-4713-8f32-dd9c9bcdc6fc","resolution":{"observed_at":"2026-08-06T22:55:00.518287Z","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":"2506.20359","last_updated":"2025-06-25T12:21:20Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-14T06:23:23.195387Z","submitted_at":"2025-06-25T12:21:20Z","title":"Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach"},"reference_resolution":{"displayed":32,"state_counts":{"malformed_identifier":1,"metadata_mismatch":2,"parse_uncertain":0,"unresolved":9,"verified_exact":4,"verified_fuzzy":16},"total_outbound_references":32},"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 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2506.20359."}