{"id":"W4366714572","doi":"10.1016/j.combustflame.2023.112755","title":"Machine learned compact kinetic models for methane combustion","year":2023,"lang":"en","type":"article","venue":"Combustion and Flame","topic":"Combustion and flame dynamics","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Siemens (Canada)","funders":"","keywords":"Methane; Combustion; Kinetic energy; Thermodynamics; Environmental science; Materials science; Chemistry; Physics; Physical chemistry; Organic chemistry; Classical mechanics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002666437,0.0002110001,0.0002608985,0.0002579448,0.0001071486,0.00005688463,0.000107245,0.0001264651,0.00005026025],"category_scores_gemma":[0.00004565078,0.0002201387,0.00008576661,0.0003927595,0.00005577115,0.0001470826,0.00002846459,0.0001990356,0.00007568445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005439444,"about_ca_system_score_gemma":0.00001061702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001127015,"about_ca_topic_score_gemma":0.00001333693,"domain_scores_codex":[0.999018,0.00003257697,0.0002631225,0.0002260437,0.0001466385,0.0003135845],"domain_scores_gemma":[0.9994097,0.0001321775,0.00003597148,0.0002109863,0.00006732631,0.0001437854],"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":[0.00007478678,0.0000354808,0.0001794707,0.0002461438,0.00005594924,0.000004653902,0.0003118512,0.933378,0.002858933,0.02107048,0.006699592,0.03508462],"study_design_scores_gemma":[0.001178909,0.00006047426,0.001774142,0.00003574225,0.00003956771,0.000009889409,0.00008406715,0.9852067,0.0002092948,0.006596722,0.004545404,0.0002591455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3450755,0.001464039,0.6378913,0.002140228,0.002249947,0.001415917,0.00021549,0.004643274,0.004904336],"genre_scores_gemma":[0.9965394,0.0009125744,0.0004591739,0.00009267555,0.00007782003,0.00002614532,0.00043318,0.00005489021,0.00140417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6514639,"threshold_uncertainty_score":0.8976997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03372895395233125,"score_gpt":0.261243290799378,"score_spread":0.2275143368470467,"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."}}