{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:NCI657T7Y3DQLZNKH5S4GSPKE7","short_pith_number":"pith:NCI657T7","schema_version":"1.0","canonical_sha256":"6891eefe7fc6c705e5aa3f65c349ea27cc7bda43e0b6c16d29becd468227a857","source":{"kind":"arxiv","id":"1606.08131","version":3},"attestation_state":"computed","paper":{"title":"Are Discrepancies in RANS Modeled Reynolds Stresses Random?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Eric G. Paterson, Heng Xiao, Jian-Xun Wang, Jin-Long Wu","submitted_at":"2016-06-27T05:54:21Z","abstract_excerpt":"In the turbulence modeling community, significant efforts have been made to quantify the uncertainties in the Reynolds-Averaged Navier--Stokes (RANS) models and to improve their predictive capabilities. Of crucial importance in these efforts is the understanding of the discrepancies in the RANS modeled Reynolds stresses. However, to what extent these discrepancies can be predicted or whether they are completely random remains a fundamental open question. In this work we used a machine learning algorithm based on random forest regression to predict the discrepancies. The success of the regressi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1606.08131","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.flu-dyn","submitted_at":"2016-06-27T05:54:21Z","cross_cats_sorted":[],"title_canon_sha256":"ba1e6d9c40bef6220fb536bd9a754360215dd385d63052be9c5e02863918f55a","abstract_canon_sha256":"5aec3924868feb51dc186078adf15069b59c711e07e3f5717af1a85be7e8bda4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:33:23.529479Z","signature_b64":"/0Jqc+qSMsfHXF7lvQVvZmObuLcWIAsSZ3RdoVoWkXSwJzq9vBlPHF4yaBrIy7lkC6UHG00RHK9V7egEa3xUDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6891eefe7fc6c705e5aa3f65c349ea27cc7bda43e0b6c16d29becd468227a857","last_reissued_at":"2026-05-18T00:33:23.528802Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:33:23.528802Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Are Discrepancies in RANS Modeled Reynolds Stresses Random?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"physics.flu-dyn","authors_text":"Eric G. Paterson, Heng Xiao, Jian-Xun Wang, Jin-Long Wu","submitted_at":"2016-06-27T05:54:21Z","abstract_excerpt":"In the turbulence modeling community, significant efforts have been made to quantify the uncertainties in the Reynolds-Averaged Navier--Stokes (RANS) models and to improve their predictive capabilities. Of crucial importance in these efforts is the understanding of the discrepancies in the RANS modeled Reynolds stresses. However, to what extent these discrepancies can be predicted or whether they are completely random remains a fundamental open question. In this work we used a machine learning algorithm based on random forest regression to predict the discrepancies. The success of the regressi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1606.08131","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1606.08131","created_at":"2026-05-18T00:33:23.528903+00:00"},{"alias_kind":"arxiv_version","alias_value":"1606.08131v3","created_at":"2026-05-18T00:33:23.528903+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1606.08131","created_at":"2026-05-18T00:33:23.528903+00:00"},{"alias_kind":"pith_short_12","alias_value":"NCI657T7Y3DQ","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"NCI657T7Y3DQLZNK","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"NCI657T7","created_at":"2026-05-18T12:30:32.724797+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NCI657T7Y3DQLZNKH5S4GSPKE7","json":"https://pith.science/pith/NCI657T7Y3DQLZNKH5S4GSPKE7.json","graph_json":"https://pith.science/api/pith-number/NCI657T7Y3DQLZNKH5S4GSPKE7/graph.json","events_json":"https://pith.science/api/pith-number/NCI657T7Y3DQLZNKH5S4GSPKE7/events.json","paper":"https://pith.science/paper/NCI657T7"},"agent_actions":{"view_html":"https://pith.science/pith/NCI657T7Y3DQLZNKH5S4GSPKE7","download_json":"https://pith.science/pith/NCI657T7Y3DQLZNKH5S4GSPKE7.json","view_paper":"https://pith.science/paper/NCI657T7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1606.08131&json=true","fetch_graph":"https://pith.science/api/pith-number/NCI657T7Y3DQLZNKH5S4GSPKE7/graph.json","fetch_events":"https://pith.science/api/pith-number/NCI657T7Y3DQLZNKH5S4GSPKE7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NCI657T7Y3DQLZNKH5S4GSPKE7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NCI657T7Y3DQLZNKH5S4GSPKE7/action/storage_attestation","attest_author":"https://pith.science/pith/NCI657T7Y3DQLZNKH5S4GSPKE7/action/author_attestation","sign_citation":"https://pith.science/pith/NCI657T7Y3DQLZNKH5S4GSPKE7/action/citation_signature","submit_replication":"https://pith.science/pith/NCI657T7Y3DQLZNKH5S4GSPKE7/action/replication_record"}},"created_at":"2026-05-18T00:33:23.528903+00:00","updated_at":"2026-05-18T00:33:23.528903+00:00"}