{"id":"W2315606277","doi":"10.1515/ijcre-2012-0003","title":"Determination of Kinetic Parameter in a Unified Kinetic Model for the Photodegradation of Phenol by Using Nonlinear Regression and the Genetic Algorithm","year":2013,"lang":"en","type":"article","venue":"International Journal of Chemical Reactor Engineering","topic":"TiO2 Photocatalysis and Solar Cells","field":"Energy","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Consejo Nacional de Ciencia y Tecnología","keywords":"Photodegradation; Kinetic energy; Nonlinear regression; Non-linear least squares; Nonlinear system; Least-squares function approximation; Reaction rate constant; Phenol; Genetic algorithm; Biological system; Mathematics; Linear regression; Estimation theory; Regression analysis; Materials science; Computer science; Thermodynamics; Applied mathematics; Chemistry; Kinetics; Catalysis; Algorithm; Statistics; Photocatalysis; Mathematical optimization; Physics; Organic chemistry","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":[0.0001766397,0.0000966778,0.0002048343,0.00009321499,0.000004288053,0.00002290341,0.0002297578,0.00005738981,0.000008207418],"category_scores_gemma":[0.000281003,0.00005727447,0.0001007564,0.00006901311,0.00004089129,0.0001072547,0.00003005088,0.0001297097,1.176178e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000469062,"about_ca_system_score_gemma":0.00001678353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001778856,"about_ca_topic_score_gemma":0.000001300352,"domain_scores_codex":[0.9990004,0.00001667835,0.0005325266,0.00007874279,0.0002851961,0.00008639703],"domain_scores_gemma":[0.9987544,0.0005220801,0.000341677,0.00009385876,0.0002587253,0.00002923794],"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":[0.0000696293,0.00004108999,0.00002261914,0.00001700022,0.00005326934,5.629542e-7,0.000239695,0.01357606,0.9594594,0.00006099753,0.000005333603,0.02645435],"study_design_scores_gemma":[0.000754535,0.0000111492,0.00004752992,0.00008003577,0.00003098352,0.0000100094,0.00001343942,0.7031264,0.2956119,0.0002357003,0.00003814947,0.00004020467],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8701974,0.0002834044,0.1291935,0.00006254383,0.00008115187,0.0001662702,0.000006680254,0.000002225198,0.000006836561],"genre_scores_gemma":[0.9655399,0.00007278059,0.03427969,0.00001158686,0.0000608952,0.00001149189,0.000006506697,0.00001354931,0.000003580297],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6895503,"threshold_uncertainty_score":0.2335585,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01260455624557629,"score_gpt":0.2428734830865264,"score_spread":0.2302689268409501,"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."}}