{"as_of":"2026-08-23T01:06:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6e5862714a4707e11eec848f204dba3f115ed88b26c3f03d4c449f1d87793392","coverage":[{"denominator":21,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":21,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T16:15:39.470002Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-22T06:32:14.747728+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2501.13415/citation-record","integrity":"/paper/2501.13415/integrity","json":"/paper/2501.13415/citation-record.json","paper":"/paper/2501.13415"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.987455Z","title":"Assessment of inner–outer interactions in the urban boundary layer using a predictive model","venue":null,"work_id":"402745bf-5849-48e1-aa0d-021aafe2715c","year":2019},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.323315Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:f730797148d47ed3bd23f05a9f94b5ab1f8550dac7fc6c61b5bce187f67e0ab0","observation_id":"3a314a5d-4b46-48bd-a6ad-ccbc05c80766","resolution":{"observed_at":"2026-08-10T16:15:40.002276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.968831Z","title":"Data-driven assessment of arch vortices in simplified urban flows","venue":null,"work_id":"4bf05d58-3fd4-4625-8d80-a5660e88e627","year":2023},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.331101Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:aab9d6e3f7d177de213f2b58e1ff0826ef2cc68769f62140eaa1ac9904e2b1b1","observation_id":"14c67ae2-ed0b-465c-9095-b19e28de26e2","resolution":{"observed_at":"2026-08-10T16:15:39.972859Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.951264Z","title":"The transformative potential of machine learning for experiments in fluid mechanics","venue":null,"work_id":"303df0f4-17e4-4107-a397-31f612e4f8b2","year":2023},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.338121Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:b4edec60a8f473aa823738591013687b390774f6d250077b3444692b78d9fd73","observation_id":"843ea6d1-02a9-4e3e-830b-4b09e6999487","resolution":{"observed_at":"2026-08-10T16:15:39.958866Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.932447Z","title":"Pedestrian exposure to black carbon and pm2","venue":null,"work_id":"bfa66180-d4f1-42a9-ba29-0cdafda440a9","year":2022},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.342904Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:26813f101fa238c6788b1cc63913744e6438e5e6cba750dfb0aa20aa434d591d","observation_id":"63d5d262-33f9-4f14-b37d-65d0c3dc9d4d","resolution":{"observed_at":"2026-08-10T16:15:39.939196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.912159Z","title":"Study of interscale interactions for turbulence over the obstacle arrays from a machine learning perspective","venue":null,"work_id":"0e84209b-cd3f-4c19-a57d-afc02935e3fe","year":2023},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.348941Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:6546c40f332d366b03a13eb882c99b7c82545534014f44de6abb82c405a6db25","observation_id":"19a810ed-36df-489d-850e-cb5f3f5c52b9","resolution":{"observed_at":"2026-08-10T16:15:39.920699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.877830Z","title":"Using machine learning to predict urban canopy flows for land surface modeling","venue":null,"work_id":"9879da77-eeda-4c06-ba20-7dfa6b9b1f8f","year":2023},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.354618Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:548141382de5c83ca5a83b1d0de43324bb802bd5d764304cec225d060ae699e6","observation_id":"467b67fe-1bf4-4f11-8928-db40d27480ac","resolution":{"observed_at":"2026-08-10T16:15:39.886214Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.849392Z","title":"A reduced order model for turbulent flows in the urban environment using machine learning","venue":null,"work_id":"d5e5e42a-eff3-4270-b767-b7fdbdb8c3b5","year":2019},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.361132Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:31454e84b467a3a3022c787ac28612e05baff6405bcb5ade736a1de7365c9c91","observation_id":"c2d04fb0-4778-4925-aa2c-c12f1ac3c909","resolution":{"observed_at":"2026-08-10T16:15:39.855860Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.826008Z","title":"Machine learning accelerated turbulence modeling of transient flashing jets","venue":null,"work_id":"54cac6be-6a60-40b0-9411-30fce761f498","year":2021},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.368638Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:67f94ce99aca0dfe6676db91b36092b7cfb6f308780fa366b60997d1af198970","observation_id":"1b6cd34e-fe74-423e-aedb-552cbd091f22","resolution":{"observed_at":"2026-08-10T16:15:39.835539Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.805826Z","title":"A novel spatial-temporal prediction method for unsteady wake flows based on hybrid deep neural network","venue":null,"work_id":"fe0c9a09-99e5-4d8e-bb2b-d9d7b1532363","year":2019},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.380324Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:3f6c45f46376776ed448b9c50d54a6b3ce4c3eae6c414711f2ed29743bae013f","observation_id":"11c87e02-a0b7-4355-8759-34e4653f99dd","resolution":{"observed_at":"2026-08-10T16:15:39.814620Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.775458Z","title":"Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow","venue":null,"work_id":"117d30f0-7f12-456e-87d6-4bf02c31da3f","year":2021},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.386017Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:d7a4b99da57dd08a608eee636821227417562b18bd92f88ba6905bda6740c964","observation_id":"2d5385bf-e4a6-4eb8-8704-641d65750329","resolution":{"observed_at":"2026-08-10T16:15:39.787836Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.755927Z","title":"Predictive models for flame evolution using machine learning: apriori assessment in turbulent flames without and with mean shear","venue":null,"work_id":"47a60e7c-51ea-43bf-a683-daeb095fc4c8","year":2021},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.393919Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:f03a55e5f60c3161b144f12876f17614f90fe4460d6c9be459ace8e2e9e33959","observation_id":"84ab712c-f95a-4e98-a02d-2285ea07964d","resolution":{"observed_at":"2026-08-10T16:15:39.763385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.406394Z","title":"Identifying regions of importance in wall- bounded turbulence through explainable deep learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.406394Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:af0da5ca48bd4fa950ae44b2c08008a4f99cfc9005ed5b96b808f7acb559fad8","observation_id":"9eccf2a0-dd0d-4753-8337-10d29b1b910e","resolution":{"observed_at":"2026-08-10T16:15:39.406394Z","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":"2026-08-10T16:15:39.722694Z","title":"The spanwise variation of roof-level turbulence in a street-canyon flow","venue":null,"work_id":"e48f01eb-0e56-4a4d-9d5b-900bc24bffa9","year":2019},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.411138Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:04efe5637e4a60b3322ae5ad58730d6d1d2b24e40307f22e182eec413c068f5d","observation_id":"41d9e7f3-d19b-41d9-a1e0-eae36086d351","resolution":{"observed_at":"2026-08-10T16:15:39.727945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.678812Z","title":"Roof-level large-and small-scale coherent structures in a street canyon flow","venue":null,"work_id":"37191c4d-c954-4fa0-af91-e2de19c98c99","year":2020},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.418932Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:7a7dc9c022129fdd5216f13e50b87e4ef82ebaf160122587fb33fb32c6056709","observation_id":"c6d8e2e2-8d27-4e6d-bf0c-0b4206eb6410","resolution":{"observed_at":"2026-08-10T16:15:39.688157Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.662344Z","title":"The flow around a surface-mounted cube in uniform and turbulent streams","venue":null,"work_id":"a0ee8df4-347b-42f1-8949-ebe79f9399ac","year":1977},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.426535Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:74531ed5eed3345de0b5581d01b34cbb9d4a15c6f0e301a7df018656cde391f4","observation_id":"44099e45-3123-4cb4-93e4-03a9eea1ea39","resolution":{"observed_at":"2026-08-10T16:15:39.666320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.643824Z","title":"Adam: Method for stochastic optimization","venue":null,"work_id":"14237c64-ca0b-4660-b08e-52b9aecf1c1d","year":2014},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.434195Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:96d03e9521a85989e7e5aca6e51ade8df2b08d957c89769ce498063d31d9ee27","observation_id":"d75b57bc-02c3-474f-9f5e-da028290f908","resolution":{"observed_at":"2026-08-10T16:15:39.648702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.621945Z","title":"Rectified linear units improve restricted boltzmann machines","venue":null,"work_id":"23a6fbf7-5a6d-4b12-b5bb-24ac539a5333","year":2010},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.439172Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:19688b5ba9b8ab16d228c8b75fec7b78615f87e224ce3301ed99c1191fdd7dc1","observation_id":"ea94da8c-2355-41d0-85c6-d2d973eb1fe4","resolution":{"observed_at":"2026-08-10T16:15:39.629439Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.598759Z","title":"Tensorflow: a system for large-scale machine learning","venue":null,"work_id":"5bac7e11-6ea4-423a-ba05-f22c96e77bb5","year":2016},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.454369Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:3848aa86abde9a3fc2bd9e8f1b3fa864937ae7a909a7bd62e5272a139f88779b","observation_id":"3a8c0c2a-47b5-4884-ac94-de6ad3386d2b","resolution":{"observed_at":"2026-08-10T16:15:39.610060Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.574140Z","title":"Street design and urban canopy layer climate","venue":null,"work_id":"714b087e-2810-4972-a855-be891dd49dd3","year":1988},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.459417Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:d9e6309d9e3204ea2a4365e3784041cb53ff2de46fcc3ec0b264c1371cc1f649","observation_id":"4cfbacd6-a56d-44dd-bc70-209e2bf7a888","resolution":{"observed_at":"2026-08-10T16:15:39.582802Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.555345Z","title":"Quadrant analysis in turbulence research: history and evolution","venue":null,"work_id":"ffe1a24c-2257-4adc-9db8-3a7d8570e4df","year":2016},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.465346Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:ded2fee1c88cc1c11a4dc959eb36a79bd8378e429c8984428956d7848638c279","observation_id":"00247ea1-1773-47fd-965d-62c3897c0fe8","resolution":{"observed_at":"2026-08-10T16:15:39.562034Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:15:39.525191Z","title":"Turbulence and the dynamics of coherent structures","venue":null,"work_id":"2f041e2f-f422-4d26-95fa-95a7fa438677","year":1987},"citing_paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T16:15:39.470002Z"},"links":{"citing_paper":"/paper/2501.13415"},"observation_digest":"sha256:c540e639998a6eb07fa0411d9267d617b74fb5c550d3e9cc117dc496edea9808","observation_id":"560e0111-f7c7-4fc6-8317-0c95513ed01d","resolution":{"observed_at":"2026-08-10T16:15:39.534748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.13415","last_updated":"2025-01-23T06:42:27Z","latest_version":1,"primary_category":"physics.flu-dyn","snapshot_observed_at":"2026-08-19T04:33:30.177819Z","submitted_at":"2025-01-23T06:42:27Z","title":"Predicting Turbulence Structure In Street-Canyon Flows using Deep Generative Modeling"},"reference_resolution":{"displayed":21,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":1,"verified_exact":0,"verified_fuzzy":20},"total_outbound_references":21},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-22T06:32:14.747728+00:00","source":"crossref"},{"observed_at":"2026-08-22T06:32:06.552537+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2501.13415."}