{"id":"W4281643232","doi":"10.1002/cjce.24483","title":"Large blade impeller application for turbulent liquid–liquid and solid–liquid mixing","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Minerals Flotation and Separation Techniques","field":"Environmental Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Impeller; Mixing (physics); Turbulence; Particle image velocimetry; Materials science; Mechanics; Blade (archaeology); Mechanical engineering; Phase (matter); Engineering; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004725339,0.0003377845,0.0002534198,0.0003805622,0.0002483312,0.0004447895,0.0002318157,0.0002701408,0.0008204076],"category_scores_gemma":[0.0005835732,0.0001464006,0.0002314703,0.0002895799,0.0002440361,0.00028249,0.0002915843,0.0002790159,0.0002310685],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002210843,"about_ca_system_score_gemma":0.0001890898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005509558,"about_ca_topic_score_gemma":0.0008105654,"domain_scores_codex":[0.9998336,0.00004699345,0.00001488437,0.00002926557,0.00004825757,0.00002700713],"domain_scores_gemma":[0.9996506,0.0001459421,0.0000535828,0.0000488342,0.00007037861,0.00003052564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002062978,0.00003093618,0.0007777697,0.00006794621,0.000004264174,0.0001068955,0.0000324493,0.001217986,0.9839079,0.0003557201,0.00006084169,0.01323101],"study_design_scores_gemma":[0.00003995565,0.0009719302,0.006532974,0.000008193383,0.00002325317,0.0002446577,0.00003948646,0.02112932,0.9664553,0.0002087615,0.004324507,0.00002161372],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9206254,0.001032526,0.07568342,0.0001364972,0.00005973052,0.00007949238,0.00007368367,0.0003423328,0.001966888],"genre_scores_gemma":[0.977845,0.0002267092,0.02137949,0.00001073352,0.00001249485,0.00001939458,0.00004315628,0.00002106678,0.0004420149],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008204076,"threshold_uncertainty_score":0.002744496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00673993201488363,"score_gpt":0.2171227780726572,"score_spread":0.2103828460577736,"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."}}