{"id":"W4400229695","doi":"10.1016/j.combustflame.2024.113569","title":"Understanding soot formation: A comprehensive analysis using reactive models in Inverse Non-Premixed Flames","year":2024,"lang":"en","type":"article","venue":"Combustion and Flame","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada; Agencia Nacional de Investigación y Desarrollo; University of Toronto","keywords":"Soot; Combustion; Inverse; Chemistry; Premixed flame; Materials science; Combustor; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002755175,0.0007283596,0.0005268458,0.000243486,0.0003870661,0.0006012967,0.0009002455,0.0007701941,0.0006624638],"category_scores_gemma":[0.0005563537,0.0003945916,0.0007448317,0.000174643,0.0004216591,0.001083193,0.0003864898,0.0006256984,0.0001551002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004282384,"about_ca_system_score_gemma":0.0007336795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009259541,"about_ca_topic_score_gemma":0.005268861,"domain_scores_codex":[0.999939,0.000007041103,0.000002512283,0.00001324313,0.00002772366,0.00001052724],"domain_scores_gemma":[0.9998564,0.00005441154,0.00002151597,0.00002449589,0.00003042478,0.00001273135],"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":[0.00007103861,0.00007925481,0.001732896,0.00008607017,0.00004614853,0.00006629779,0.00005689916,0.9350274,0.05065052,0.00673437,0.0001479863,0.005301159],"study_design_scores_gemma":[0.00000653351,0.0000126711,0.0004971151,0.000002159717,0.000005888377,0.000008941772,0.000007261468,0.9938659,0.004077702,0.001346486,0.0001640008,0.000005291392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6959422,0.001096935,0.2900052,0.0003124153,0.00007946316,0.00009362923,0.0004282052,0.0006503252,0.01139161],"genre_scores_gemma":[0.9847222,0.0004147582,0.01309825,0.00003122519,0.00001309351,0.00002340874,0.000154217,0.00008413316,0.001458628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009259541,"threshold_uncertainty_score":0.01841134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1058266176978689,"score_gpt":0.288587936730432,"score_spread":0.1827613190325631,"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."}}