{"as_of":"2026-08-17T21:31:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:111a91f4e66bc76147de6180fad532fe27d1e5239b75e86ead123181ac767b4d","coverage":[{"denominator":58,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":58,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:26:30.640229Z","state":"measured"},{"denominator":58,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":58,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2505.01261/citation-record","integrity":"/paper/2505.01261/integrity","json":"/paper/2505.01261/citation-record.json","paper":"/paper/2505.01261"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:26:32.245108Z","title":null,"venue":null,"work_id":"59b638db-e36d-4af9-aaf3-b7ca3a07cd92","year":2022},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.236279Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:331138f5c937ac481dccc0f02fc5dd49af0d270df56346d0b5a9d68dbbad3285","observation_id":"0a23238c-54d2-46ee-8529-c553657c0439","resolution":{"observed_at":"2026-08-16T04:26:32.249328Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:32.228337Z","title":"Trabelsi, M","venue":null,"work_id":"33515d63-0f8f-4299-a7ec-14cbf1c63916","year":2021},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.246042Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:54d671d8d46cca08a73ef7e427bde5b4722d300284c498a388000eb5ea8c0c49","observation_id":"6dbb5808-dc7c-43f5-8d88-46e1e7a9bc79","resolution":{"observed_at":"2026-08-16T04:26:32.234103Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1088/1742-6596/364/1/012098","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:26:30.719415Z","title":null,"venue":null,"work_id":"273070ce-2fad-4ce5-93a2-1b5e5f5f8145","year":2012},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.252746Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:3c7e88f4f564e5ca2ce66026d6b8f4768748cffbeb253ebffca56c693544c9a8","observation_id":"03256edb-154a-4421-9418-8594a1146da9","resolution":{"observed_at":"2026-08-16T04:26:30.731249Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:32.211211Z","title":null,"venue":null,"work_id":"2d286dd0-ede7-4f93-8edf-cc2ea02114ea","year":2019},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.260858Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:d9e4ced41e1759fcb25330397707ed6f74aa6b6831b1ba6edca9dba4b5f64e34","observation_id":"2073091e-2968-447d-b9b4-cc5f98e96563","resolution":{"observed_at":"2026-08-16T04:26:32.216935Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:32.190621Z","title":"Zolghadri, M","venue":null,"work_id":"a650c812-7093-4e35-bd51-001da44f5152","year":2023},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.268460Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:3e917d3d6b644ac4e39c53d99bb511a4f9882e08c414a68990bbb079afc82c7f","observation_id":"14a2e53d-6fa6-4c66-b4b6-a8e365684017","resolution":{"observed_at":"2026-08-16T04:26:32.196754Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:32.168961Z","title":null,"venue":null,"work_id":"6a3fb37c-6e77-47ad-90d1-bb224fd7b196","year":2020},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.277329Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:c37557e76eefd6cd52757cd97a1da95a3390fbaf60f05bdc5f4c0b31f4ef96c3","observation_id":"0c14e7c5-2810-480a-8bdf-fc3445c0727d","resolution":{"observed_at":"2026-08-16T04:26:32.176142Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:32.143439Z","title":"Zolghadri, S.-A","venue":null,"work_id":"201a9609-4995-4a62-8726-a16d316aa5a5","year":2021},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.283446Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:6d5cc8db49bb6bbfb4aeeb168abea8396c48df3eb3b491c27a390339b4c033df","observation_id":"69ed05ef-c728-476d-ac5f-65d6d596fb5c","resolution":{"observed_at":"2026-08-16T04:26:32.152374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:32.122322Z","title":"´Zróbek, Remarks about methods of recognizing types of depre ciation and obsolescence, Studia i Materiały Towarzystwa N aukowego Nieruchomo´sci (2011) 65–72","venue":null,"work_id":"66aa8301-527b-49b0-bd9c-018a79009f2b","year":2011},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.289200Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:ce1782de4f1abba8e1467488e5252b5bdfe9c45ecdd94f1f2888b74c0def7c67","observation_id":"096ccf00-fd42-47d3-bb83-523be48527fe","resolution":{"observed_at":"2026-08-16T04:26:32.128539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:32.085146Z","title":null,"venue":null,"work_id":"ed7d6e58-f4ec-4847-acf6-5ffc3968399f","year":null},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.294557Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:b7d68641fd724957f755b239b15256801c03bae9a0a68e5e863b5bf97d94eaf7","observation_id":"9e0f0328-2394-4bb9-9fa9-2e76a0d72a41","resolution":{"observed_at":"2026-08-16T04:26:32.098499Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:32.053127Z","title":null,"venue":null,"work_id":"55cd9b3e-f83d-4fbc-b504-47d59575545c","year":2022},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.300533Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:355909b8d5fd7c75c969543782bdb84465dc5e85e91248c546952e777f7b9f39","observation_id":"c7f23f95-eab2-4c20-b774-e9dca79e2ef6","resolution":{"observed_at":"2026-08-16T04:26:32.060385Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:32.024327Z","title":"Jenab, K","venue":null,"work_id":"65118ec9-94b2-49f0-98f8-1699ccaa899d","year":2014},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.306118Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:13c70c06bab85f8665f6aa04663ed17d8d2d34837e9fbefc22b81227cad94795","observation_id":"45846e79-c20d-459d-ba95-609bb85f9a66","resolution":{"observed_at":"2026-08-16T04:26:32.034352Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.996695Z","title":null,"venue":null,"work_id":"fe2ff3fe-1956-4aee-8d9f-3f372e5c8452","year":2019},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.312363Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:1a44bd2c04e1d767eb72a0a7a03474b9474ce55ee1cf5ca629c5c2092650fa4a","observation_id":"2fe8d2b9-601f-407d-9516-66af6a35bc63","resolution":{"observed_at":"2026-08-16T04:26:32.004468Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.970240Z","title":null,"venue":null,"work_id":"bb4c294c-e78f-448c-a057-d2eb63fdc298","year":2019},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.317256Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:74a14e0c1be7319330494a87f331392f1425cc27623fa0054dda684729e852f0","observation_id":"c0f3f5d6-3408-4f87-99b7-1b237ef3fdb7","resolution":{"observed_at":"2026-08-16T04:26:31.978294Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.945192Z","title":"Tchuente, J","venue":null,"work_id":"749f9c3d-ff65-4a35-9c09-0392cfab4bba","year":2024},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.324196Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:09e7fe273b78eddd62dac5583baa294fed51827febb9570e766063e7bafda43e","observation_id":"5071bebf-cdc2-4919-8913-65d769c7ea95","resolution":{"observed_at":"2026-08-16T04:26:31.954113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.919922Z","title":"Culot, M","venue":null,"work_id":"e683af56-1442-45ad-96c4-7d449338a8ee","year":2024},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.329723Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:0ff5e3c8436090e95667cb61b0e7759a9195f16169d043c9b7819808439996fa","observation_id":"a520d779-efd1-4256-9701-015e17545e48","resolution":{"observed_at":"2026-08-16T04:26:31.928147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.856487Z","title":"Trabelsi, B","venue":null,"work_id":"4d3547dc-f381-4d6f-93ff-4964a183e674","year":2021},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.336279Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:26e4823a070b7ecf63eb55908a4cf619efe04d6c8f2df6e178d32f518407acf5","observation_id":"55a36d8f-6aae-45f6-97aa-fff44b715756","resolution":{"observed_at":"2026-08-16T04:26:31.887387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.778787Z","title":"Jennings, D","venue":null,"work_id":"a4441466-d675-4d0d-834c-6f446d3198b5","year":null},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.344620Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:4c2322d23c6a849d34dab47896db733137b419ad894a2dd4685ea4e89b2ccd09","observation_id":"05d8fa15-4cca-4022-b81c-cfda656ed316","resolution":{"observed_at":"2026-08-16T04:26:31.807907Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.748167Z","title":null,"venue":null,"work_id":"4f32e419-bb06-4b1e-a765-ad9b231b13ff","year":2022},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.351227Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:bbbe56a52feef0d9dfb129ee947097b9f37b15e6a5a3c4ba16174d94100cf3b7","observation_id":"91351828-f530-45d7-83a3-08c1ae72e91d","resolution":{"observed_at":"2026-08-16T04:26:31.754487Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:30.356495Z","title":"Zhou, Machine learning, Springer nature, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.356495Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:e07d95a6537634a50798a9b3ad02bcaa5b112c916fe4393c1d77609c419d2318","observation_id":"3315d227-166d-4cd4-8e1c-2300f4f3666d","resolution":{"observed_at":"2026-08-16T04:26:30.356495Z","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":"2026-08-16T04:26:31.707607Z","title":"Hubauer, S","venue":null,"work_id":"8584aedf-50d0-47bd-a1f2-c92f539db828","year":2013},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.361655Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:26a246d7c4a6b5ccf8d296da2970126790846f9c848ae7346c2731903f60e8bf","observation_id":"dfea22c3-7c65-42be-bc56-eefec0133763","resolution":{"observed_at":"2026-08-16T04:26:31.716764Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.671147Z","title":null,"venue":null,"work_id":"b03d2b72-5f20-4cee-9c97-370a6fa7b0e7","year":2015},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.367407Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:5729c29bb25222f44ddadb9ff92f0c5df5e42c42199fd393491e1a59ace9499f","observation_id":"bc8cc79b-ed24-421a-bf94-9d0f53517010","resolution":{"observed_at":"2026-08-16T04:26:31.683750Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.631836Z","title":"Libes, S","venue":null,"work_id":"26173a29-2750-4de5-aa12-6e7bef4ac02a","year":2015},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.373403Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:48a581a2081de44add07d9d7e9f07cec5731b48dd5a51f438260d6ee7007f4c4","observation_id":"fbd92de4-247b-412c-b4d5-332ec4b5e515","resolution":{"observed_at":"2026-08-16T04:26:31.642840Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.607665Z","title":"Grichi, Y","venue":null,"work_id":"0859f8d1-a2bf-46a8-8ba7-67a00fbced32","year":2017},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.379395Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:11d030261741b6d3cdcf90be56b59e04ed5c0351e2f7d885947cc0d593d8d411","observation_id":"2b8bcc88-757d-4f43-a75c-814bf731fa32","resolution":{"observed_at":"2026-08-16T04:26:31.614548Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.567231Z","title":null,"venue":null,"work_id":"873cef6a-8dfe-4f12-9e5e-c3327f6e26b4","year":2022},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.385861Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:b4ca3b7db770dcd84f587ecc75a1c0738463778ebb82818ee6259e79ce918bd7","observation_id":"bbd229b0-b3d4-4166-8dc2-81c20a2d992c","resolution":{"observed_at":"2026-08-16T04:26:31.577914Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.537713Z","title":null,"venue":null,"work_id":"2b87e568-af40-4762-8b22-a6e64a22acc5","year":2022},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.392108Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:351942cb5b822ca5acb06189cd1253a8254eac178675bf60b07ff2ab21364cce","observation_id":"d1fa55ed-c0a0-40a9-9b8c-15b7ef97d28a","resolution":{"observed_at":"2026-08-16T04:26:31.547637Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.504816Z","title":"Cholaquidis, R","venue":null,"work_id":"5627e142-b760-40c7-97d0-4285e7f45808","year":2020},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.397279Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:97ee9d79a3f9af3e501c7c7af4f8e7b98d12ee9e83636abe579909ba1599b3a1","observation_id":"192f99dd-f243-4f82-860d-28a431aea4f8","resolution":{"observed_at":"2026-08-16T04:26:31.516088Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:30.402887Z","title":"Breiman, Random forests, Machine learning 45 (2001) 5–32","venue":null,"work_id":null,"year":2001},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.402887Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:b5bf6726d97f9f331a3757a3126052eb243b5e5246d0e7797f0366e927cdffc1","observation_id":"8a6f462e-f119-4780-9fb6-ce16e3238059","resolution":{"observed_at":"2026-08-16T04:26:30.402887Z","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":"2026-08-16T04:26:31.446100Z","title":"Grichi, Y","venue":null,"work_id":"08b466e4-0284-4412-a8df-28edf1d2d51a","year":2018},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.408907Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:590ad498e251c9d7b9b1e24dacd507d30b10e960b5f4afa4dff551eab8065a92","observation_id":"d83f994c-f36d-49f4-befa-1e5cc2bf5808","resolution":{"observed_at":"2026-08-16T04:26:31.464131Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.418230Z","title":"Grichi, T.-M","venue":null,"work_id":"1649849b-0598-498d-8d21-1a0cbe19810c","year":2018},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.414812Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:f86446b9c56c021448483512654c805a4eda6d81523ed9370ce656c14eb9f391","observation_id":"ce0c4426-c297-41e7-b685-8a2d24a79e48","resolution":{"observed_at":"2026-08-16T04:26:31.428379Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.385103Z","title":"Sierra-Fontalvo, A","venue":null,"work_id":"71f2be68-1de0-4e6a-985a-3e2276f7f109","year":2023},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.420774Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:84a35d7329337660ee75d28f498be07ac8a15e2a680d0183297650bb18c90ee9","observation_id":"2f82aae7-250b-4fac-ba24-1d0e88fbd9a1","resolution":{"observed_at":"2026-08-16T04:26:31.396645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:30.428179Z","title":"Goodfellow, Deep learning, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.428179Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:74068143cb5424f03eac6f3c7dc0ab0188f3c185ae0a8065b4b97af4f0b57d78","observation_id":"4ab99741-e64c-45dd-a52e-a5b035b89d95","resolution":{"observed_at":"2026-08-16T04:26:30.428179Z","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":"2026-08-16T04:26:31.338813Z","title":null,"venue":null,"work_id":"2919dfff-e4e0-41fb-8cf4-f595be49f411","year":2023},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.435547Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:825ff52d07b652a5f1cbf2e57fd34717eb178976801d10365f65b3f3cbc1d380","observation_id":"0a74ca56-2008-4675-8acc-c95561d63a71","resolution":{"observed_at":"2026-08-16T04:26:31.350042Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.08803","last_updated":"2017-02-27T23:21:10Z","snapshot_observed_at":"2026-08-10T10:35:06.780085Z","submitted_at":"2016-05-27T21:24:32Z","title":"Density estimation using Real NVP","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.08803","snapshot_observed_at":"2026-08-16T04:26:30.443143Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.443143Z"},"links":{"cited_paper":"/paper/1605.08803","citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:296c6ae50efa861c4096d84762f5769f1011a668a126fa0fb608fefc033c34a8","observation_id":"86103c41-fa2d-4842-8723-0f2f56b2ad08","resolution":{"observed_at":"2026-08-16T04:26:30.443143Z","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":"2026-08-16T04:26:31.317766Z","title":"Xu, et al., Synthesizing tabular data using conditio nal GAN, Ph.D","venue":null,"work_id":"db5aea3f-0892-43bd-96a0-2f89f097147d","year":2020},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.450137Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:dc9d27c1e62cad4b81797543b675f0ce4f2397f44075e2a5387b5b1ceb42ff49","observation_id":"e87f9903-1c85-4c40-8f19-4661f45dd433","resolution":{"observed_at":"2026-08-16T04:26:31.322981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.297083Z","title":null,"venue":null,"work_id":"2377cff1-c8a6-4a2d-89c3-910ef9e495d5","year":2024},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.458308Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:be7bcc7489f1fc58a45255421d4dc3a84d516313504db545ec5304bf30b54890","observation_id":"d28b3f80-eac9-4f02-8d16-0396597ca147","resolution":{"observed_at":"2026-08-16T04:26:31.304323Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.278233Z","title":"Srinivas, R","venue":null,"work_id":"c4d90665-0865-415a-aa6a-558536aa682f","year":2015},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.468037Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:868bd9ec6661443a07d1791fb01eebb78a410a936328d859fa4b4a34825017fa","observation_id":"ffa7f74c-a9d8-4520-9af3-cb608a848909","resolution":{"observed_at":"2026-08-16T04:26:31.284768Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.251960Z","title":"Srivastava, G","venue":null,"work_id":"21f61974-c7d7-41c2-b1f0-c8f0c098dfab","year":2014},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.477778Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:5e7290536b29db632badea1e206b690ea4becc2e3480ec04acf4d752504ec88e","observation_id":"4c8a69c4-0879-4959-a839-612683c669fa","resolution":{"observed_at":"2026-08-16T04:26:31.260037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.223724Z","title":"Janakiramaiah, G","venue":null,"work_id":"c616d8a3-cc18-4163-a5b5-e797ea7841d7","year":2020},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.485329Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:6e6c05905a0a438faad89583c39744f44f38ca3313a682dd922c30557bc3d574","observation_id":"76f20343-14fd-4a78-ba2d-14bbf990a0c3","resolution":{"observed_at":"2026-08-16T04:26:31.232089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.206746Z","title":null,"venue":null,"work_id":"807e6dc7-6715-4a7b-9f8f-5adf367e7c5d","year":2016},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.492872Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:2b3a689994703838904897656134d32f93a73a8106b54fe08e06b44073b9dd9f","observation_id":"1b6a5462-6735-4255-9854-dbe5b3904251","resolution":{"observed_at":"2026-08-16T04:26:31.211624Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.183799Z","title":"Fournier, D","venue":null,"work_id":"40af0a10-2c21-476e-bfe5-a1b626c42825","year":2019},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.498382Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:cc4313e372a3a28ac404d30e7b22b1c1548070fb4cd659347a328fc69e785ce0","observation_id":"eccdeff9-f700-4c36-9b1a-97df8215baf7","resolution":{"observed_at":"2026-08-16T04:26:31.191719Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2202.12040","last_updated":"2025-02-14T09:55:19Z","snapshot_observed_at":"2026-08-16T17:18:57.902212Z","submitted_at":"2022-02-24T11:40:44Z","title":"Self-Training: A Survey","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2202.12040","snapshot_observed_at":"2026-08-16T04:26:30.503291Z","title":"Amini, V","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.503291Z"},"links":{"cited_paper":"/paper/2202.12040","citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:46ad73cff1a21aa7d7693a4a8d72eda2c5f422b3149670dc3391f1c0e0810dde","observation_id":"410d4a48-73a8-4fce-b1ab-6b2db610efed","resolution":{"observed_at":"2026-08-16T04:26:30.503291Z","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":"2026-08-16T04:26:31.161003Z","title":null,"venue":null,"work_id":"3488754a-ec6d-4091-b09b-00b4bdf6b8c8","year":2014},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.511560Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:f84e1a997f1ef94725468d1a40402bd6b948cccdf38e015c537a450e278ea99f","observation_id":"93ea9607-d739-4b46-a6b8-b3f83d4c6e03","resolution":{"observed_at":"2026-08-16T04:26:31.168586Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.131268Z","title":"Carrara, J","venue":null,"work_id":"f86912af-7a2b-485b-9935-b84cd55ee62d","year":2020},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.517881Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:272e3c5c64beacad7058781b273b73852e7df7b98119bd259b57e6d6ee083464","observation_id":"30305ce4-ccaf-4aa2-8af5-2eba6f03a812","resolution":{"observed_at":"2026-08-16T04:26:31.141343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.109040Z","title":null,"venue":null,"work_id":"6e015255-2506-4e65-a5cc-7e7e7537a2cb","year":2011},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.524368Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:488bcf3b569b120fcc2f338cedadd387d2c0689fe2f2a9e4b98715e7acc30423","observation_id":"9ee44f23-3af9-4d29-be50-55bd89b5b754","resolution":{"observed_at":"2026-08-16T04:26:31.116109Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.083524Z","title":"Chakraborty, Topsis and modiﬁed topsis: A comparati ve analysis, Decision Analytics Journal 2 (2022) 100021","venue":null,"work_id":"c404999f-013e-4715-93df-09b45ef6c538","year":2022},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.535148Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:6740e9dbacc6c471356d0ba513c2a1d88d2ec42893c466592fa2cbc3984705bf","observation_id":"60cc22a9-1216-43d3-8a0f-cf9063972b22","resolution":{"observed_at":"2026-08-16T04:26:31.092494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.056068Z","title":"Hodges Jr, The signiﬁcance probability of the smirno v two-sample test, Arkiv för matematik 3 (1958) 469–486","venue":null,"work_id":"d2e681c4-7254-416c-924c-76bfca0e5d3e","year":1958},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.541605Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:c7af60d8a45e4fcafca0786a096eeeee474c1da63cfd7a9e09b08e5d03ac750f","observation_id":"6c2d6c49-141f-4ee7-8aea-d2e170ca321d","resolution":{"observed_at":"2026-08-16T04:26:31.063959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.029808Z","title":null,"venue":null,"work_id":"7eb49904-36fa-4dc7-a2a1-5ef6099a1b0d","year":2016},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.548940Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:78b62fc4cdaacb38f0e4853fd7e023ed5c01a3540b8f68c7bb09f0be2c2620f3","observation_id":"041eb2d5-98d2-42a3-b499-09e35e0d198c","resolution":{"observed_at":"2026-08-16T04:26:31.039673Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:31.006203Z","title":"Taboga, Lectures on probability theory and mathemat ical statistics, (No Title) (2017)","venue":null,"work_id":"51db4787-e800-49d5-bf30-668e566c25f7","year":2017},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.560129Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:918c2a2fab52ac9ae2bfc306941d3c73d1bfb5b65024f819790cc556d3c83bce","observation_id":"00df2e3b-c92b-4d92-a163-2dd435d3ebdd","resolution":{"observed_at":"2026-08-16T04:26:31.012695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10015","last_updated":"2025-04-03T00:24:10Z","snapshot_observed_at":"2026-08-16T19:48:08.722236Z","submitted_at":"2023-05-17T07:49:16Z","title":"Utility Theory of Synthetic Data Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10015","snapshot_observed_at":"2026-08-16T04:26:30.570177Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.570177Z"},"links":{"cited_paper":"/paper/2305.10015","citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:ff4d643f8d0f8cb30738927dff9a066fd21bf676c3861bed2f3355434ba2aee5","observation_id":"931d0bf8-c288-40d1-80ca-ebb7638b2b0f","resolution":{"observed_at":"2026-08-16T04:26:30.570177Z","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":"2026-08-16T04:26:30.979249Z","title":"Shalev-Shwartz, S","venue":null,"work_id":"67d4c644-114b-497a-89ec-e194f927958e","year":2014},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.579173Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:e4dd0eb62b63aef154d757dd5196cb5ab1144ca3953eecaeff64fb64b566a0d6","observation_id":"f80c888e-e37a-48e4-b963-ef4f2cc3ab69","resolution":{"observed_at":"2026-08-16T04:26:30.985271Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:30.956624Z","title":"Hastie, S","venue":null,"work_id":"edde4faf-e07b-410a-8e61-e147806f5b33","year":2009},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.589102Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:63afb47d211d466e0b3b70a6c3e99cd778702682449b838a4245a39fe35c63c4","observation_id":"b1240f8d-99ba-41cf-a290-834954bd3a42","resolution":{"observed_at":"2026-08-16T04:26:30.964289Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.15017365","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:26:30.689646Z","title":"Saad, Zenner diod obsolescence dataset, 2024","venue":null,"work_id":"6681cdd1-1a30-4be6-b595-15270957fba9","year":2024},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.595908Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:23df9f84cdc3f40dae674a7d178e435df976860ead416bf5e6a62b6904ebdefb","observation_id":"874d957a-1247-4457-8f92-4a44199647c9","resolution":{"observed_at":"2026-08-16T04:26:30.699387Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:30.935161Z","title":"Smirnov, Table for estimating the goodness of ﬁt of em pirical distributions, The annals of mathematical statist ics 19 (1948) 279–281","venue":null,"work_id":"145b0077-7698-423e-9e2c-06ddf1c3aab4","year":1948},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.602834Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:674a29316f99d2ebe67b208adeba4b4171db5e54f7ebec488fdd44711283dcd2","observation_id":"d3e38ebd-ed73-454e-94c7-7f5fc2cc08c8","resolution":{"observed_at":"2026-08-16T04:26:30.943440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:30.911260Z","title":"Hastie, R","venue":null,"work_id":"86cb7288-dacd-4867-b229-decdf1bfa1f1","year":2009},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.611313Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:4137260c894d894b2a45531047911a75ab78cadd5cf5c946c9ea67bd5229b143","observation_id":"31ee1330-2d6e-4b99-942c-d44b091500a9","resolution":{"observed_at":"2026-08-16T04:26:30.919932Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:30.889852Z","title":null,"venue":null,"work_id":"4ed27eb0-befa-4d41-8f4a-14e4a2df6d5d","year":2018},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.619316Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:906e19da3bc30a25a35c802524e96059234df433dd4c95cd37916075f05b3a7e","observation_id":"833be871-f397-420e-87da-1088d98ba25b","resolution":{"observed_at":"2026-08-16T04:26:30.896286Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:30.871956Z","title":"Calders, S","venue":null,"work_id":"45876ab7-8aa6-44d6-a4ad-5711fb2a2353","year":2007},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.627490Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:8d25e08c0b6da28fbff944c4d271cbd1eb7b3371201bcced0b9ea52266104d6e","observation_id":"99921aed-29b0-4bd9-94ee-989c1c942b75","resolution":{"observed_at":"2026-08-16T04:26:30.877657Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:30.854670Z","title":"Papamakarios, E","venue":null,"work_id":"33bea48a-1305-4090-9f80-089f358b804c","year":2021},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.633985Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:56511bee7434b180b06ceac030d1e5df5553bd24bd3dcf7eb083095d9784a965","observation_id":"d3f60843-5d11-47bd-bfbe-0c7f51581878","resolution":{"observed_at":"2026-08-16T04:26:30.860283Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:26:30.829952Z","title":"Kobyzev, S","venue":null,"work_id":"65624b78-1f59-4910-95ba-9176bca9d62b","year":2020},"citing_paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-16T04:26:30.640229Z"},"links":{"citing_paper":"/paper/2505.01261"},"observation_digest":"sha256:18370501c6654c9262b9dcc2821b4dc27f92e51023c8e768f8c3888456036a0b","observation_id":"475b181d-a32a-4bd2-ad9c-cca170e7cc32","resolution":{"observed_at":"2026-08-16T04:26:30.837376Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2505.01261","last_updated":"2025-05-02T13:28:50Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-16T04:19:52.150075Z","submitted_at":"2025-05-02T13:28:50Z","title":"Enhancing Obsolescence Forecasting with Deep Generative Data Augmentation: A Semi-Supervised Framework for Low-Data Industrial Applications"},"reference_resolution":{"displayed":58,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":2,"verified_fuzzy":32},"total_outbound_references":58},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 58 of 58 outbound references and 0 inbound Pith citation observations for arXiv:2505.01261."}