{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006197362,0.0007867489,0.00126182,0.0004978463,0.000436427,0.00102493,0.001336676,0.001404828,0.002523406],"category_scores_gemma":[0.004067645,0.0006508575,0.0006602646,0.000622369,0.0006718875,0.001608285,0.00104653,0.001703351,0.0006546545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001237436,"about_ca_system_score_gemma":0.0008856196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01022608,"about_ca_topic_score_gemma":0.01158133,"domain_scores_codex":[0.9998068,0.00005892989,0.00001684942,0.00004181793,0.00004979864,0.00002579238],"domain_scores_gemma":[0.9983175,0.001177776,0.0001715102,0.0001253,0.0001643133,0.00004366802],"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":[0.00004095603,0.00001375449,0.0001183211,0.0000227481,0.00001064644,0.00001338895,0.000009673588,0.9891265,0.0002133127,0.002792866,0.0001981363,0.007439585],"study_design_scores_gemma":[0.000001831749,0.000001950563,0.00001665692,0.000001234243,8.87885e-7,0.000001142514,6.601279e-7,0.9989591,0.00005297008,0.0009212491,0.00004141306,9.659258e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1417689,0.002525041,0.8472786,0.0009022786,0.0001914679,0.00005043337,0.001152714,0.001768039,0.00436245],"genre_scores_gemma":[0.9622368,0.0004976909,0.03112011,0.00007846793,0.00007738649,0.0001084194,0.0009930691,0.0001157962,0.004772156],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01022608,"threshold_uncertainty_score":0.02033311,"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."}}