{"id":"W2048952392","doi":"10.1016/j.ces.2010.03.007","title":"Polymer powders mixing part II: Multi-component mixing dynamics using RGB color analysis","year":2010,"lang":"en","type":"article","venue":"Chemical Engineering Science","topic":"Rheology and Fluid Dynamics Studies","field":"Chemical Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ternary operation; Mixing (physics); Homogeneity (statistics); RGB color model; Materials science; Binary number; Biological system; Mathematics; Artificial intelligence; Computer science; Statistics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004437874,0.0003786621,0.0004975479,0.0004274021,0.0004255664,0.00006253958,0.0007604721,0.0002376241,0.00004700464],"category_scores_gemma":[0.0006406706,0.0003858582,0.0002360148,0.00196374,0.0006397607,0.0002851046,0.0004897375,0.0008276864,0.00001039906],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002767613,"about_ca_system_score_gemma":0.0000626362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004972743,"about_ca_topic_score_gemma":0.000008183416,"domain_scores_codex":[0.9973807,0.000006279446,0.000463126,0.0007403189,0.0004287304,0.0009807865],"domain_scores_gemma":[0.9987749,0.0001660338,0.00008946902,0.0005209306,0.00012077,0.0003278326],"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":[0.00000528482,0.00005494265,0.001205257,0.00002178764,0.0001400993,0.00000696378,0.0001629425,0.07590643,0.9177659,0.004635707,0.000003996657,0.00009076553],"study_design_scores_gemma":[0.0002260072,0.000006308533,0.0002419507,0.00002276801,0.0001315366,0.00001334294,0.00002539423,0.8027173,0.1962174,0.000004649964,0.00001755205,0.0003758417],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8089998,0.0001158153,0.1895616,0.00009785686,0.0007461893,0.00008506647,0.0000118448,0.0002821346,0.00009964449],"genre_scores_gemma":[0.9498952,0.000004562792,0.0495821,0.00004585612,0.0001296036,0.00001535216,0.00001568742,0.00003754066,0.0002741241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7268109,"threshold_uncertainty_score":0.9998593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01035985262090025,"score_gpt":0.2342355374115867,"score_spread":0.2238756847906864,"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."}}