{"id":"W3154868116","doi":"10.1002/cjce.24138","title":"Multi‐objective <scp>Deng's</scp> grey incidence analysis, orthogonal optimization, and artificial neural network modelling in hot‐maceration‐assisted extraction of <scp>African</scp> cucumber leaves ( <scp> <i>Momordica balsamina</i> </scp> )","year":2021,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Mean squared error; Maceration (sewage); Backpropagation; Grey relational analysis; Taguchi methods; Artificial intelligence; Approximation error; Mathematics; Pattern recognition (psychology); Statistics; Computer science; Biological system; Engineering; Biology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005269288,0.0003847344,0.0008046946,0.0005431881,0.0002055551,0.0002052668,0.0003743936,0.0003136358,0.0000377072],"category_scores_gemma":[0.002762067,0.0003749085,0.0002966972,0.002855083,0.0001740167,0.0003852598,0.00005412977,0.001115383,0.000001003229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004171106,"about_ca_system_score_gemma":0.0006094658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001120747,"about_ca_topic_score_gemma":0.001880767,"domain_scores_codex":[0.9972128,0.00007157238,0.001095467,0.000406688,0.0005183409,0.0006951488],"domain_scores_gemma":[0.9963853,0.001497275,0.0006411698,0.000302548,0.0006036228,0.0005701284],"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":[0.000004722535,0.00004346217,0.006285569,0.0000519232,0.0005028454,0.00009368954,0.0006147346,0.8638577,0.1283199,0.00003810812,0.0001390007,0.00004826858],"study_design_scores_gemma":[0.0004237538,0.00001984869,0.002185092,0.0001081639,0.0009414249,0.0002732529,0.0008939909,0.7182242,0.2766693,0.00006350086,0.0001141763,0.00008339288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9506787,0.003500693,0.04522618,0.00006358961,0.0001479972,0.00007385653,0.00003229433,0.0000253135,0.0002513767],"genre_scores_gemma":[0.9930149,0.0001121423,0.006036175,0.00004651757,0.0005112083,0.000008812515,0.00004217268,0.00004909209,0.000178953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1483493,"threshold_uncertainty_score":0.9998703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01559867288391918,"score_gpt":0.2346498946826587,"score_spread":0.2190512217987396,"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."}}