{"as_of":"2026-08-08T02:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a7cf7e1987a46c9c23209a883cbe3cfb86c778817a518bc73d0946cf3caecf33","coverage":[{"denominator":29,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":29,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:18:35.824380Z","state":"measured"},{"denominator":29,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":29,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+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/2506.05609/citation-record","integrity":"/paper/2506.05609/integrity","json":"/paper/2506.05609/citation-record.json","paper":"/paper/2506.05609"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:18:42.365896Z","title":"Procura por seguro viagem dis- para no primeiro bimestre de 2024,","venue":null,"work_id":"8a4835fb-724d-4f7f-bc11-85c6fc62f750","year":2024},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:32.057585Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:f5aa96e050cffbfd195971c3fc58d84baf28ff5fc33c1b7a2af35dd70cf9d21d","observation_id":"01932267-b460-4e6c-beb2-77af4666a752","resolution":{"observed_at":"2026-08-07T10:18:42.450998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:42.160197Z","title":"Tendências na indústria de seguros 2023,","venue":null,"work_id":"d88e8be0-eed9-4525-a511-08ebe61c8f1c","year":2023},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:32.188491Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:975dce781451135ac0178d2d93a382792112735e5d33656e94437f76f5e1a8ba","observation_id":"3766a769-152b-4389-a16f-ae04f3dab9da","resolution":{"observed_at":"2026-08-07T10:18:42.261867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:41.941294Z","title":"Predicting travel insurance pur- chases in an insurance firm through machine learning methods after covid-19,","venue":null,"work_id":"f690637f-c52d-4a54-b93f-a7c6a59cae4e","year":2023},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:32.299673Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:7fc65fa05a419fbf74518bfd2daa6333d209737d7bf7122667f4cac24a71a1d9","observation_id":"c2194764-2ec8-4ab3-935a-7064028bcfd7","resolution":{"observed_at":"2026-08-07T10:18:42.060461Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:41.743354Z","title":"Exploring the potential of machine learning techniques for predicting travel insurance claims: A comparative analysis of four models,","venue":null,"work_id":"3e5de5ec-4639-4833-af36-349d78c3b5b7","year":2023},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:32.416604Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:8de0a7fa1027c086ecc2955f69738f9ada8387ac51d5d047754129015cf22ce1","observation_id":"ce7d63b8-3128-4436-bdb7-25362126d0d1","resolution":{"observed_at":"2026-08-07T10:18:41.815629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:41.475781Z","title":"Insurance risk prediction using machine learning,","venue":null,"work_id":"34838314-7860-4053-b176-56f4ddc44dd5","year":2023},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:32.565321Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:c75f434814d1b8e51a7c85e36196601005585319d0dc97f6059edf168ab89bf6","observation_id":"bf0303e6-1768-413d-a801-32a62f200b05","resolution":{"observed_at":"2026-08-07T10:18:41.583238Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:41.228101Z","title":"Research on changes in travel insurance premiums driven by climate change: A case study of hong kong region,","venue":null,"work_id":"938bb6cf-463f-48bd-9778-a27c44796d13","year":2024},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:32.741241Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:62d9fe42d462fa13a69bda388cf7531952c334af4623d134b019901fc1476adb","observation_id":"b2dde2ab-5b28-418c-8fee-7e1e7657f16c","resolution":{"observed_at":"2026-08-07T10:18:41.367515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:41.003045Z","title":"A machine-learning-based business ana- lytical system for insurance customer relationship management and cross-selling,","venue":null,"work_id":"6ca26227-681c-441e-a6af-9de5653c0ba6","year":2023},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:32.910518Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:acf07dacddf2ad072c5678948da5fba35c2b20543b321853f6980d74e1f6e0d5","observation_id":"a963a0d9-2e96-49f6-b858-e42b14a65f58","resolution":{"observed_at":"2026-08-07T10:18:41.124085Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:40.869017Z","title":"Random forests,","venue":null,"work_id":"7b907af7-bf86-4e28-aef9-b79b5eb8365d","year":2001},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.024483Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:12983ed5999ecb3305941f38b9a74bd636cdf0057404f5fee1d0707d96a914b0","observation_id":"137491a0-5390-4335-8047-67a1f7a7e315","resolution":{"observed_at":"2026-08-07T10:18:40.921541Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:40.705436Z","title":"Xgboost: A scalable tree boosting system,","venue":null,"work_id":"a3340735-7e2e-4cb5-8d2b-edc44c9d4973","year":2016},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.142137Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:70d89fddcf623a76cc893ae0ffd86beb43384f8fd8be5c8bfd140aac0b6e6986","observation_id":"5999a158-9838-4d5c-8808-182a6dd561c8","resolution":{"observed_at":"2026-08-07T10:18:40.767278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:40.531598Z","title":"Lightgbm: A highly effi- cient gradient boosting decision tree,","venue":null,"work_id":"6613944a-8355-45e3-97db-03988f3d07a7","year":2017},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.282909Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:2f609f32616de74529660c34c3aeeca2c9b3da6760b0b12b9697625c0ea03dfe","observation_id":"a27194d9-26a0-4ee1-a13b-1f76864952fa","resolution":{"observed_at":"2026-08-07T10:18:40.622180Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:40.345808Z","title":"glmnet: Lasso and elastic-net regularized generalized linear models,","venue":null,"work_id":"42989af3-2873-45a7-a87e-ec6073ed780d","year":2024},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.423139Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:d6e8d2728d19b2571a5dfb7a0d0ea64ae27edf2514d69f0872b83dec9ea618da","observation_id":"e4b89873-5df9-4c93-96f4-caacde48a578","resolution":{"observed_at":"2026-08-07T10:18:40.433387Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:40.186335Z","title":"R: A language and environment for statistical computing,","venue":null,"work_id":"f0cd196d-cb48-493c-940d-fff775dd9596","year":2024},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.545991Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:ac6d9b016bb7041c8e94fd7f322ef0e5d806c7a08e2154b17fb45aab1aa9ebd8","observation_id":"c57f1390-ca19-47e0-a3e6-bfd7101d9be6","resolution":{"observed_at":"2026-08-07T10:18:40.278778Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:39.949717Z","title":"Regularization paths for generalized lin- ear models via coordinate descent,","venue":null,"work_id":"343951af-9179-40c6-a195-2869bb5de194","year":2010},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.698231Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:a9e2dcc9d272fc95ae920547fd3f554be18bed4b4544138d24553ace379f6f2a","observation_id":"9c793839-b711-486e-b817-f55edd915ae4","resolution":{"observed_at":"2026-08-07T10:18:40.011774Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:39.735458Z","title":"Regression shrinkage and selection via the lasso,","venue":null,"work_id":"c3145c54-a39f-4205-a77a-99cfb070a0f0","year":1996},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:33.859757Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:7de023d80b57f3f798704d571bd49f3f9f83cbc428006d2fa697eae481d55b99","observation_id":"5c9e009c-0232-4b5d-a02e-6859786129ad","resolution":{"observed_at":"2026-08-07T10:18:39.838410Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:39.514082Z","title":"Regularization and variable selection via the elastic net,","venue":null,"work_id":"95a0e6cf-d579-4d23-9295-0dd8d09676e9","year":2005},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.036951Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:8e087076ce861d5d5283b1abe4f85561a230313eccb864be7b74a7bffe1e2abc","observation_id":"08461fea-8987-4b75-92eb-fc74a9f9a0f4","resolution":{"observed_at":"2026-08-07T10:18:39.627911Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:39.305747Z","title":"Random search for hyper-parameter optimization,","venue":null,"work_id":"369b5015-f2bd-431d-9336-58f558049ee0","year":2012},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.146982Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:4eae994989023c125654ff84ac2afd6dd48ebcb257b8fd3c9a01b68a116059f9","observation_id":"c7cf3961-2706-4b1b-a403-e26fe8d9ff71","resolution":{"observed_at":"2026-08-07T10:18:39.386606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:39.041990Z","title":"Algorithms for hyper-parameter optimization,","venue":null,"work_id":"09dd6f56-0dc4-4e21-a469-37facae97e50","year":2011},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.286817Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:6af40f190439017e7196121672510102af68f752cf3dd2811e9bed8c3816c7f1","observation_id":"e41a3487-184c-42bc-accf-a8ae9f17149a","resolution":{"observed_at":"2026-08-07T10:18:39.175985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:38.656135Z","title":"Ridge regression: Biased estimation for nonorthog- onal problems,","venue":null,"work_id":"092170ca-92bb-4437-aa31-659fe3c1cec3","year":1970},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.422839Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:084b30252f19432596ac275864f6a18e8b6cfd069a10351447a14d8565b7e8b6","observation_id":"8bdf1692-de7a-406a-8544-0b22446a6658","resolution":{"observed_at":"2026-08-07T10:18:38.862642Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:38.415921Z","title":"A study of cross-validation and bootstrap for accuracy estimation and model selection,","venue":null,"work_id":"6c095b2f-6aff-402b-b7d3-641222e2d0e8","year":1995},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.519935Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:edccf9789f731891b0dc10791f0b2caaa70343510114f68920d94907158beb1e","observation_id":"c23470a9-21ff-4bff-8822-04f80200c128","resolution":{"observed_at":"2026-08-07T10:18:38.540866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:38.087471Z","title":"The use of the area under the roc curve in the evaluation of machine learning algorithms,","venue":null,"work_id":"ff0e59a6-dbbb-495e-aa5e-1fb1c06b1024","year":1997},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.629834Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:0ec098beb73e9bc533d3953bf58b0b5ec801e95c5901034570deb73e9516f6d2","observation_id":"336cff41-edaa-4fdf-9c46-acc15db8228d","resolution":{"observed_at":"2026-08-07T10:18:38.296397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:37.752189Z","title":"Regularization paths for generalized lin- ear models via coordinate descent,","venue":null,"work_id":"fcf8e0eb-68dc-4e27-9845-ba3c953b9cc5","year":2010},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.746055Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:6bcb69a4f53ff84acbcbed4fccf3f7d00b4eebf53fb065ee26aee25b057cb610","observation_id":"ccc19320-c81f-469d-b646-7eab780ddcd9","resolution":{"observed_at":"2026-08-07T10:18:37.924313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:34.902015Z","title":"Hastie, R","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:34.902015Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:2fe22108deb7bb680343ccf50b408af7af08fcc608384466c3705b688253fc69","observation_id":"9d32b787-1b1b-4dd9-a7a3-40a1cfd82a6b","resolution":{"observed_at":"2026-08-07T10:18:34.902015Z","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":"2026-08-07T10:18:37.421745Z","title":"James, D","venue":null,"work_id":"b6ad9d15-8eb4-45f0-b7ff-8e8bb2d8c913","year":2013},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.021920Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:8e452f9adb5cbe0ed2e00506cf37b673e82f1993560911a65a73131a6c606c3a","observation_id":"ecd33b65-e36d-4a15-913a-f8fba36003b5","resolution":{"observed_at":"2026-08-07T10:18:37.542817Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.11363","last_updated":"2018-10-24T13:08:24Z","snapshot_observed_at":"2026-08-05T11:52:23.809651Z","submitted_at":"2018-10-24T13:08:24Z","title":"CatBoost: gradient boosting with categorical features support","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.11363","snapshot_observed_at":"2026-08-07T10:18:35.135219Z","title":"Catboost: gradient boosting with cate- gorical features support,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.135219Z"},"links":{"cited_paper":"/paper/1810.11363","citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:70560e46274960524a68aeb1e9bc95004e4a9e7496d1ce095f5db8172e7045f4","observation_id":"6b0fe3cf-457b-4b62-ac9d-fc279baba05a","resolution":{"observed_at":"2026-08-07T10:18:35.135219Z","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":"2026-08-07T10:18:37.126386Z","title":"Catboost: Unbiased boosting with categorical features,","venue":null,"work_id":"c8522667-ccf6-4a1e-a326-8728133ff867","year":2018},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.281589Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:15c38c788e3befd000a7dcadd0056a7a1efe3012d3d0b2ddad4659be61f1557e","observation_id":"dcf74b62-1691-477a-a332-e611a9dd47cc","resolution":{"observed_at":"2026-08-07T10:18:37.273949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:36.917581Z","title":"Permutation importance: a cor- rected feature importance measure,","venue":null,"work_id":"5643f7d2-7836-48e0-8f78-765436fd526f","year":2010},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.408904Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:37ce5d6a3af6f50b604e60010586d681dd6ea7a9a6098646b14e7e31c325346a","observation_id":"b5b53990-55d7-42e3-9e72-6ab3486d9ea0","resolution":{"observed_at":"2026-08-07T10:18:36.996939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:36.680723Z","title":"A unified approach to interpreting model predictions,","venue":null,"work_id":"54c5b021-e6d5-4cd6-9be5-75e3584d11e2","year":2017},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.527321Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:35cb8e6ffcc22bdad5ac38a971e8d81af8228b911e1e908af1fabd9cd50ecc4d","observation_id":"fd34c0a7-6186-48b6-a609-44977eba7647","resolution":{"observed_at":"2026-08-07T10:18:36.768695Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:36.364766Z","title":"Hybrid non-heuristic variable selection models via regularization for black box models applied to the insurance sector,","venue":null,"work_id":"affe4556-d1ec-4807-a885-319f588a6bd0","year":2025},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.686699Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:846a16349e9940af74981953916072df41d0828189d8efad3823fab99618d91a","observation_id":"a4aa7c9f-e8ee-45b5-9ed0-1ab2d3904d23","resolution":{"observed_at":"2026-08-07T10:18:36.481260Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+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":"2026-08-07T10:18:36.069455Z","title":"Multivariate adaptive regression splines,","venue":null,"work_id":"05636bbb-f742-4440-bca3-354a39008440","year":1991},"citing_paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T10:18:35.824380Z"},"links":{"citing_paper":"/paper/2506.05609"},"observation_digest":"sha256:cb10900617bf1b6e3a4c54e68ede35483ca1539557982eed3baa2ba4e1d39f48","observation_id":"25f13f30-6ee9-4528-a56f-33fbfcb78f86","resolution":{"observed_at":"2026-08-07T10:18:36.193036Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.05609","last_updated":"2025-06-05T21:45:31Z","latest_version":1,"primary_category":"stat.AP","snapshot_observed_at":"2026-08-07T10:20:24.934016Z","submitted_at":"2025-06-05T21:45:31Z","title":"Non-Heuristic Selection via Hybrid Regularized and Machine Learning Models for Insurance"},"reference_resolution":{"displayed":29,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":0,"verified_fuzzy":27},"total_outbound_references":29},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2506.05609."}