{"id":"W2165237509","doi":"10.1002/cjce.22286","title":"Droplet size scaling in a turbulent pipeline flow","year":2015,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Particle Dynamics in Fluid Flows","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Schlumberger (Canada)","funders":"","keywords":"Breakup; Turbulence; Scaling; Reynolds number; Mechanics; Flow (mathematics); Pipeline (software); Inertial frame of reference; Physics; Weber number; Statistical physics; Classical mechanics; Mathematics; Geometry; Engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0005359432,0.0001405621,0.000217367,0.0001216422,0.00001488433,0.00004460023,0.0003386277,0.00008146171,0.00001238306],"category_scores_gemma":[0.0005606664,0.0001208848,0.00008096527,0.0002674917,0.00002818174,0.00009546922,0.00001155534,0.0005380407,0.000009242847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006341554,"about_ca_system_score_gemma":0.0001949141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002473393,"about_ca_topic_score_gemma":0.0003047932,"domain_scores_codex":[0.9989318,0.00001109979,0.0003949414,0.00006677379,0.0002021656,0.0003932331],"domain_scores_gemma":[0.9990387,0.0001069025,0.00002860109,0.0001638998,0.00005965151,0.0006022524],"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":[0.000003764781,0.000003328619,0.0001112836,0.00001132621,0.00002032359,0.0001841105,0.0004803729,0.9838175,0.01420774,0.00008321486,0.000689683,0.000387418],"study_design_scores_gemma":[0.000440555,0.000006807182,0.00005243111,0.00008111696,0.00001361135,0.0002054131,0.00001783297,0.9909065,0.007086188,0.0001743315,0.0008671265,0.0001480426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9965452,0.0008087457,0.001385391,0.0004327387,0.0005566959,0.00005382862,0.000003018095,0.00003342899,0.0001809684],"genre_scores_gemma":[0.9972146,0.000002307502,0.002449523,0.00004958687,0.000235658,0.000002310509,8.542995e-7,0.00003761621,0.000007525012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007121556,"threshold_uncertainty_score":0.492954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01103444946387174,"score_gpt":0.1926088964021405,"score_spread":0.1815744469382688,"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."}}