{"id":"W4404704250","doi":"10.1002/cjce.25556","title":"Development of a deep neural network and empirical model for predicting local gas holdup profiles in bubble columns","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Fluid Dynamics and Mixing","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Range (aeronautics); Quadratic equation; Computer science; Current (fluid); Empirical modelling; Moment (physics); Quadratic function; Experimental data; Quadratic model; Algorithm; Artificial intelligence; Machine learning; Mathematics; Engineering; Simulation; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002854912,0.0001075265,0.0001820383,0.0001039633,0.00002963599,0.00004137785,0.0001183982,0.0000674754,0.000001570982],"category_scores_gemma":[0.00004062933,0.00009301116,0.00004768183,0.0001480446,0.00002989572,0.00006625565,0.00001345458,0.0002976795,1.081037e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002045757,"about_ca_system_score_gemma":0.0001908533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004410265,"about_ca_topic_score_gemma":0.0008259125,"domain_scores_codex":[0.9991811,0.000003028911,0.0003547387,0.00007088867,0.00008625538,0.0003039872],"domain_scores_gemma":[0.9996166,0.00008722341,0.00002054475,0.00005214109,0.00002966929,0.0001938213],"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":[0.000003746851,0.000001299669,0.0001124511,0.0001682755,0.00003143127,0.00001133689,0.00112396,0.9862091,0.008674775,0.0001964969,0.00006186539,0.00340526],"study_design_scores_gemma":[0.0001328679,0.000007924996,0.00004900431,0.0002312411,0.00001398073,0.00005214527,0.00002269733,0.9971351,0.002012204,0.0001170479,0.0001326771,0.00009310281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8706324,0.00177973,0.1271834,0.00008317184,0.0001839192,0.00009371134,0.000004773925,0.00001962306,0.0000192736],"genre_scores_gemma":[0.9870633,0.000003151065,0.01276532,0.00001022972,0.0001152789,0.000007741723,0.0000013864,0.00003076932,0.000002789036],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1164309,"threshold_uncertainty_score":0.3792885,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01087888214449098,"score_gpt":0.1994684207517424,"score_spread":0.1885895386072514,"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."}}