{"id":"W2406683702","doi":"10.1061/(asce)hy.1943-7900.0001171","title":"CFD Approach for Column Separation in Water Pipelines","year":2016,"lang":"en","type":"article","venue":"Journal of Hydraulic Engineering","topic":"Water Systems and Optimization","field":"Engineering","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Pipeline transport; Separation (statistics); Water column; Column (typography); Petroleum engineering; Computational fluid dynamics; Environmental science; Marine engineering; Geology; Hydrology (agriculture); Geotechnical engineering; Mechanics; Engineering; Environmental engineering; Computer science; Oceanography; Mechanical engineering; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002525975,0.00009958828,0.0001876175,0.0001800279,0.00001068071,0.00002273666,0.00008108441,0.00005932745,0.000004875279],"category_scores_gemma":[0.00002128441,0.00006092669,0.00006121286,0.00006609492,0.000003395718,0.0002915583,0.000006526487,0.00006090374,0.000002519784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007261329,"about_ca_system_score_gemma":0.000005265562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001697604,"about_ca_topic_score_gemma":0.000006746769,"domain_scores_codex":[0.9992551,0.000006119269,0.0004047067,0.00006076798,0.0000965799,0.0001767008],"domain_scores_gemma":[0.9997672,0.0000241694,0.00003779039,0.0000692121,0.00005763712,0.00004392364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007197804,0.000008417116,0.0001268181,0.00008167263,0.00001743146,0.000002578408,0.0001813064,0.9707021,0.02731634,0.00001852132,0.0011457,0.0003919466],"study_design_scores_gemma":[0.001172377,0.00006162401,0.0004264444,0.0001595659,0.00001391005,0.00006678707,0.00001945405,0.940825,0.04338295,0.00002472055,0.01365047,0.0001967361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08337648,0.0001767194,0.9153685,0.00007742926,0.0005126613,0.0001343948,0.000001291926,0.0000447665,0.0003077867],"genre_scores_gemma":[0.9900444,0.00002863668,0.009279539,0.000006375877,0.0003972424,0.00001331555,0.0000023941,0.00002959017,0.0001985774],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9066678,"threshold_uncertainty_score":0.2484518,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007605884910652214,"score_gpt":0.2014653771071146,"score_spread":0.1938594921964624,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}