{"id":"W4390349458","doi":"10.1016/j.partic.2023.12.010","title":"Analysis of cohesive particles mixing behavior in a twin-paddle blender: DEM and machine learning applications","year":2023,"lang":"en","type":"article","venue":"Particuology","topic":"Granular flow and fluidized beds","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Paddle; Mixing (physics); Materials science; Computer science; Mechanics; Composite material; 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":[],"consensus_categories":[],"category_scores_codex":[0.0001689478,0.00008460998,0.0002560217,0.000330694,0.0000374431,0.000006660625,0.00005676869,0.00005873128,0.00003483467],"category_scores_gemma":[0.0000322792,0.00008849146,0.0000527742,0.001025629,0.00005315594,0.00003285334,0.0000315169,0.0001086972,0.00002624428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000124226,"about_ca_system_score_gemma":0.000005436136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004184075,"about_ca_topic_score_gemma":0.0002712282,"domain_scores_codex":[0.9992731,0.00005005542,0.0002343169,0.0001430708,0.00005817665,0.0002413293],"domain_scores_gemma":[0.9996367,0.000148487,0.00002379881,0.0001229165,0.00001707093,0.00005106908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007207082,0.00003459574,0.8984114,0.00002567244,0.0001914919,0.00001446848,0.0007287114,0.0169216,0.07833798,0.0004395467,0.00001317152,0.004874166],"study_design_scores_gemma":[0.0005718409,0.00004427458,0.4249454,0.000006589868,0.0009246182,0.00000605458,0.0003470123,0.5409921,0.03077046,0.000214409,0.0009690303,0.0002082323],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957941,0.0005987916,0.003200197,0.00004516064,0.00002288348,0.000132706,0.000007873571,0.0001399228,0.0000584311],"genre_scores_gemma":[0.9993802,0.0001324093,0.0002192693,0.000008880305,0.000008528428,0.0001968949,0.00003039199,0.00001313393,0.00001029881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5240704,"threshold_uncertainty_score":0.3608577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02021394297977644,"score_gpt":0.2466878831202471,"score_spread":0.2264739401404706,"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."}}