{"id":"W2904466932","doi":"10.1007/s12182-018-0276-4","title":"Numerical study of crude oil batch mixing in a long channel","year":2018,"lang":"en","type":"article","venue":"Petroleum Science","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"China National Offshore Oil Corporation; Shell Canada; Natural Sciences and Engineering Research Council of Canada; Syncrude; Suncor Energy Incorporated; Canadian Natural Resources Limited","keywords":"Mixing (physics); Mechanics; Reynolds-averaged Navier–Stokes equations; Computational fluid dynamics; Laminar flow; Channel (broadcasting); Fluent; Light crude oil; Reynolds number; Turbulence; Physics; Chemistry; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003144853,0.0004099858,0.0006537355,0.0005029984,0.0006458773,0.0007976039,0.0006428194,0.001286503,0.001388481],"category_scores_gemma":[0.0008355562,0.0002373509,0.0004596392,0.0005202891,0.0009109023,0.0004399262,0.0005801796,0.0005052208,0.0001359408],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008495548,"about_ca_system_score_gemma":0.001004543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01566051,"about_ca_topic_score_gemma":0.007043747,"domain_scores_codex":[0.9998598,0.0000233076,0.000007935195,0.00002968817,0.00003333824,0.00004582631],"domain_scores_gemma":[0.9993056,0.0003947306,0.00009335066,0.00003275323,0.00009240019,0.00008102445],"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":[0.0001642814,0.00009213723,0.004397641,0.00003864145,0.0000155744,0.0002824343,0.00005469591,0.9868382,0.005364302,0.00109237,0.0001116853,0.001548092],"study_design_scores_gemma":[0.00001082618,0.00004091953,0.0006725381,0.000002922554,0.000003110245,0.00001327581,0.00002402585,0.9983923,0.0006624489,0.00009663356,0.00007477441,0.000006184522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9743541,0.0002151858,0.01939532,0.000160879,0.00003879795,0.00003952817,0.0002133293,0.0002084826,0.005374352],"genre_scores_gemma":[0.9931498,0.00005338086,0.005079985,0.00001522456,0.000006054214,0.00002592105,0.00008924594,0.00001149238,0.001568958],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01566051,"threshold_uncertainty_score":0.03113872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01436632445662812,"score_gpt":0.2785407169972988,"score_spread":0.2641743925406707,"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."}}