{"id":"W3217653213","doi":"10.32920/ryerson.14662842.v1","title":"Using electrical resistance tomography to characterize and optimize the mixing of micron sized polymeric particles in a slurry reactor","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Impeller; Homogeneity (statistics); Materials science; Slurry; Mixing (physics); Rotational speed; Slip factor; Agitator; Composite material; Mechanical engineering; 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":[0.0004629816,0.0003003697,0.0002953913,0.0002499183,0.0001077833,0.0004781033,0.000246889,0.0003981268,0.0002887099],"category_scores_gemma":[0.0007894615,0.0001891769,0.0002279483,0.0002723366,0.0002296674,0.0007107009,0.0002229978,0.0004163621,0.0001585952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002490962,"about_ca_system_score_gemma":0.0001829825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002637921,"about_ca_topic_score_gemma":0.0004122776,"domain_scores_codex":[0.9997256,0.00005780887,0.00002276113,0.00005946859,0.0001114868,0.00002287158],"domain_scores_gemma":[0.9996628,0.0001513818,0.00009575149,0.00002296287,0.00005527942,0.0000119579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004068502,0.0000171827,0.0004705512,0.00003343857,0.000002873721,0.00003124776,0.00001474998,0.0007530215,0.9949555,0.00006387748,0.00001708877,0.003599754],"study_design_scores_gemma":[0.000006702506,0.0001802982,0.001483543,0.000002766311,0.000008832808,0.00006310094,0.00003157734,0.01586725,0.9819092,0.00003996488,0.0004002276,0.000006648197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9175356,0.0005832327,0.08043292,0.0001288874,0.00001526795,0.00004350674,0.00009223598,0.000246237,0.000921932],"genre_scores_gemma":[0.9472722,0.0003793121,0.05161696,0.00003368987,0.000006501028,0.00004312618,0.00008204589,0.00003429225,0.0005317479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0004781033,"threshold_uncertainty_score":0.002448499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01911071683237422,"score_gpt":0.228698963864321,"score_spread":0.2095882470319468,"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."}}