{"id":"W4377042735","doi":"10.1002/cjce.24956","title":"Kinetic modelling: Regression and validation stages, a compulsory tandem for kinetic model assessment","year":2023,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Catalysis for Biomass Conversion","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Région Normandie","keywords":"Kinetic energy; Biochemical engineering; Regression analysis; Regression; Biological system; Computer science; Experimental data; Chemistry; Process engineering; Mathematics; Statistics; Machine learning; Engineering; Physics","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":[0.0002529756,0.0001304864,0.0001822666,0.0002267524,0.0000518974,0.000050344,0.0001623552,0.00007509753,0.000003767049],"category_scores_gemma":[0.00003355044,0.0001064696,0.00007808378,0.0002064883,0.0000256882,0.0000771167,0.00001481902,0.0002068677,0.000001094695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002241668,"about_ca_system_score_gemma":0.00008302349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006137799,"about_ca_topic_score_gemma":0.00001138484,"domain_scores_codex":[0.9992434,0.000005395492,0.0002588121,0.00008424644,0.0001600043,0.0002481426],"domain_scores_gemma":[0.9994182,0.00008851936,0.00005015282,0.0001222374,0.00006314995,0.0002577246],"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":[0.000002239932,0.000001097589,0.00001980782,0.00005302079,0.00002849077,0.000006203567,0.0001068849,0.9241284,0.07462111,0.0001349042,0.0005517941,0.0003460586],"study_design_scores_gemma":[0.0002844816,0.00001233162,0.00002834868,0.00009316691,0.00004580103,0.00002690276,0.000008911883,0.9723157,0.02627978,0.0002996214,0.000489026,0.000115938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8955767,0.0004173846,0.10325,0.0002773871,0.0002265743,0.0001421155,0.00001256164,0.00006677805,0.00003058001],"genre_scores_gemma":[0.9961628,0.00001948165,0.003643271,0.00001072702,0.00008587114,0.000004974332,0.00002203844,0.00003491492,0.00001587374],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1005862,"threshold_uncertainty_score":0.4341703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0210337393376034,"score_gpt":0.2243325189712687,"score_spread":0.2032987796336653,"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."}}