{"as_of":"2026-08-09T00:36:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:658657aa613261c86a8eb21249f59ebf0fcbf7249aa4a733112c85d1aa6a1fd4","coverage":[{"denominator":19,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":19,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:23:02.533996Z","state":"measured"},{"denominator":19,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":19,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+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/2506.05933/citation-record","integrity":"/paper/2506.05933/integrity","json":"/paper/2506.05933/citation-record.json","paper":"/paper/2506.05933"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.35378/gujs.789519","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:03.819927Z","title":"Gazi University Journal of Science 34(3), 710– 716 (Sep 2021)","venue":null,"work_id":"93de4597-c972-42d3-a905-618671712dd4","year":2021},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:01.047296Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:9cbd1a022d6798e58dff1fd7797c122165f38538bba832f1b07c780b4a1c0f16","observation_id":"ee30a0e0-fb09-4c61-bfcc-1b3d69be6c20","resolution":{"observed_at":"2026-08-07T10:23:03.823533Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1111/mice.12370","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:03.793928Z","title":"Computer- Aided Civil and Infrastructure Engineering 33(10), 833–848 (2018)","venue":null,"work_id":"6adbe6a4-6f97-4cdb-a178-f3b3ba40bf40","year":2018},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:01.087004Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:778393112e8f5d4be3b1947c8919f8baa713be602b8d32a1527eafb5f6a26288","observation_id":"0a284246-a7b3-4c17-a260-03fc7232a7a4","resolution":{"observed_at":"2026-08-07T10:23:03.812538Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T10:23:04.439276Z","title":"Yale University Press (1956), https://trid.trb.org/View/91120, number: 226 pp Machine Learning Predictions for Traffic Equilibria 15","venue":null,"work_id":"bfa36451-b3dc-498f-ab0a-9d818ce3c983","year":1956},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:01.141132Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:32a02a5579114e8750106c0eb97b92b1a2717f547794d84e6bddc28b9adad2fd","observation_id":"c2d98094-87fe-4404-ba2a-534a1eb98f8a","resolution":{"observed_at":"2026-08-07T10:23:04.442678Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"stable/2576926","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:04.406743Z","title":"Transportation Science 39(4), 446–450 (2005), https://www.jstor.org/stable/25769266, publisher: INFORMS","venue":null,"work_id":"e0d8933a-e278-43a0-b0a6-e88f23422eb2","year":2005},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:01.245240Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:26f8c3e942db569886ebbeb5a58a9cab8c2e05a79021fb9f1d63e01a25dadf69","observation_id":"8b1c1823-07ce-4e58-b96e-db55257905e4","resolution":{"observed_at":"2026-08-07T10:23:04.414959Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2024.10483","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:04.325116Z","title":"Transportation Research Part C: Emerging Technologies 167, 104838 (Oct 2024)","venue":null,"work_id":"f34ee586-9ece-4709-b83a-1de66b0a4331","year":2024},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:01.354572Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:ca7ab3bcd941fc9d17fb725d654fc7c295234a9682f5d2786dcaff13ffb1bad3","observation_id":"6fbb0d56-cdfc-4625-9113-261153c4b555","resolution":{"observed_at":"2026-08-07T10:23:04.329832Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"8667.2004","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:04.256050Z","title":"Computer-Aided Civil and Infrastructure En- gineering 19(6), 446–455 (2004)","venue":null,"work_id":"ad321966-9d08-4367-92b9-f18f73465648","year":2004},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:01.431541Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:9de239a8c77837bdaecfbf36ad321da2e04ff3337baf9a71b50f339feadbc252","observation_id":"044715b3-fa1a-41aa-b186-b5652125774e","resolution":{"observed_at":"2026-08-07T10:23:04.261666Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.ijtst.2016.06.003","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:03.604107Z","title":"International Journal of Transportation Science and Technology 5(1), 17–27 (Aug 2016)","venue":null,"work_id":"7280724e-79f3-4dd4-ba69-6065860dc8ed","year":2016},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:01.464316Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:52abea042e2831293f7b7e16c7ff9325cb8b02e9ec4782eb1c3c820171386428","observation_id":"9d5fd654-05a5-4d15-a647-02cd200fb10b","resolution":{"observed_at":"2026-08-07T10:23:03.685808Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1111/mice.12444","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:03.400441Z","title":"Computer-Aided Civil and Infrastructure Engineering 34(8), 638–653 (Aug 2019)","venue":null,"work_id":"d61a3ff1-6c2d-47cf-a483-154d0e57451f","year":2019},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:01.560098Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:169c5b4f8802ece5c151c1794a6f04a1ef40bd0db82ddad950e237f0ea0c39c5","observation_id":"217f1cf3-44e1-4ad9-9d0b-ae8cf02fb620","resolution":{"observed_at":"2026-08-07T10:23:03.467128Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T10:23:01.625192Z","title":"Transportmetrica A: Transport Science 11(1), 74–101 (Jan 2015)","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:01.625192Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:657653cacff3f5b8b61d798dd3a7ff52e0b1adcfca3d80c6b435b3e85cf882a4","observation_id":"53695960-b8da-4396-8f1f-a27394a49068","resolution":{"observed_at":"2026-08-07T10:23:01.625192Z","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":"9935.2017","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:04.097852Z","title":"Transportmetrica A: Transport Science 14(4), 346–371 (Apr 2018)","venue":null,"work_id":"5e55aad3-f82f-4876-965d-19bef72c513b","year":2018},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:01.708967Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:141054112d1806a0c989d717b0bf4424f61e2d5a2ab158a90e5a1cf195f85bde","observation_id":"0b2badab-89af-4be0-bb56-12f6e40d72e3","resolution":{"observed_at":"2026-08-07T10:23:04.102894Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T10:23:01.868777Z","title":"Transportation Research 9(5), 309–318 (Oct 1975)","venue":null,"work_id":null,"year":1975},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:01.868777Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:5e703bc0530c78ba900b418f13aafb7bb63cc10a7d89650c5fd24b9181c6133c","observation_id":"0cd66497-f9b2-4cfd-83eb-1adcbc2b117a","resolution":{"observed_at":"2026-08-07T10:23:01.868777Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1108/srt-01-2021-0004","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:03.158579Z","title":"Smart and Resilient Transportation3(2), 118–130 (Jan 2021)","venue":null,"work_id":"66ce09da-9187-4317-ad84-f19b1a716d01","year":2021},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:01.961081Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:819ac685e754f03bbb97412e023fbbdde3c3a8c59f6a603479ef84004984e844","observation_id":"0bf048d0-c5c1-43d5-b947-42886d7d4937","resolution":{"observed_at":"2026-08-07T10:23:03.250840Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1371/journal.pone.0164780","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:02.970749Z","title":"PLOS ONE 11(10), e0164780 (Oct 2016)","venue":null,"work_id":"411f354f-1d2c-4e39-b1e5-69de4494ea60","year":2016},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:02.047668Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:637e0f4e62f3d80b980d1bc3890edc168a54141dae1183787af441a6d5eb9284","observation_id":"0665558c-2067-4545-ae67-3faf81a2fd43","resolution":{"observed_at":"2026-08-07T10:23:03.051056Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1111/mice.12740","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:02.805492Z","title":"Computer-Aided Civil and Infrastructure Engineering 37(4), 427–450 (2022)","venue":null,"work_id":"4c1df75e-8cb7-4845-bd72-bd7bccdeb712","year":2022},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:02.138494Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:8fd058c1a9ac58744e26b2e555a2e5af3df73202c4a2e918d7c4b708213159ed","observation_id":"bdf0e005-6b4b-4cb7-91a1-09303aea1d15","resolution":{"observed_at":"2026-08-07T10:23:02.878469Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11057","last_updated":"2025-02-17T09:47:35Z","snapshot_observed_at":"2026-07-06T20:23:04.425190Z","submitted_at":"2025-01-19T14:21:33Z","title":"Machine Learning Surrogates for Optimizing Transportation Policies with Agent-Based Models","version":2},"cited_work":{"arxiv_id":"2501.11057","doi":"10.48550/arxiv.2501.11057","metadata_source":"pith","pith_arxiv_id":"2501.11057","snapshot_observed_at":"2026-08-07T18:16:16.850658Z","title":"Machine Learning Surrogates for Optimizing Transportation Policies with Agent-Based Models","venue":"cs.CE","work_id":"4be876e4-b8e8-4395-b85a-e58c915c71a3","year":2025},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:02.251397Z"},"links":{"cited_paper":"/paper/2501.11057","citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:982fd279c820ba21b104696fab950629ec026ca91bbc06d0e03713ead23159dd","observation_id":"650c5f70-8477-4c03-bc73-3eed3c779cae","resolution":{"observed_at":"2026-08-07T10:23:02.677791Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"stable/2576774","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:04.021847Z","title":"Transportation Science 8(3), 203–216 (1974), https://www.jstor.org/stable/25767747, publisher: INFORMS","venue":null,"work_id":"ce2dce51-1b18-4c21-ab61-087ca0b0f0fc","year":1974},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:02.342524Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:510c4694d516cd1f7ffb5ca974ed319580e852543a6b9d9452ea265b2d9bad8c","observation_id":"4b9e6993-275d-43da-95e5-d54f368abc51","resolution":{"observed_at":"2026-08-07T10:23:04.027629Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T10:23:04.429958Z","title":null,"venue":null,"work_id":"2bf028d8-7c8f-4352-81b5-c0f0584f1525","year":1964},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:02.373242Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:cb0cb30f8a0b987c7f0c2fb4d9eb11c5cb26dc23c026843172cef6142b828bb3","observation_id":"98bf0ca1-d3f8-4d97-b735-76721f228ce4","resolution":{"observed_at":"2026-08-07T10:23:04.432761Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+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":"2026-08-07T10:23:04.420763Z","title":null,"venue":null,"work_id":"054757fa-3ac7-40be-ae40-68e9e2c0a411","year":2018},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:02.438505Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:137359e6e569c9cc81ff4801c8bfad8fcb5ed6aa5e247b1a9fa111baba29edd3","observation_id":"7baff75b-74cb-4b74-af61-23c2564b085d","resolution":{"observed_at":"2026-08-07T10:23:04.423573Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"2014.23182","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:23:03.944303Z","title":"IEEE Trans- actions on Intelligent Transportation Systems 15(6), 2595–2604 (Dec 2014)","venue":null,"work_id":"0b6d301f-48e1-4732-97fb-78a3adcb52a2","year":2014},"citing_paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T10:23:02.533996Z"},"links":{"citing_paper":"/paper/2506.05933"},"observation_digest":"sha256:b0d9bd30283f50f3efd7f32e0d10578e5a3086b84852f47ea009cfa74dcb0b40","observation_id":"34cba43a-5fde-4e23-aa4a-2eb90c2d235c","resolution":{"observed_at":"2026-08-07T10:23:03.950803Z","resolver_source":"raw_fallback","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.05933","last_updated":"2025-06-06T09:59:05Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-08T04:43:38.758908Z","submitted_at":"2025-06-06T09:59:05Z","title":"Machine Learning Predictions for Traffic Equilibria in Road Renovation Scheduling"},"reference_resolution":{"displayed":19,"state_counts":{"malformed_identifier":0,"metadata_mismatch":3,"parse_uncertain":0,"unresolved":4,"verified_exact":11,"verified_fuzzy":1},"total_outbound_references":19},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2506.05933."}