{"id":"W4306754169","doi":"10.1016/j.fuel.2022.126187","title":"Laminar Flame Speed modeling for Low Carbon Fuels using methods of Machine Learning","year":2022,"lang":"en","type":"article","venue":"Fuel","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Laminar flow; Laminar flame speed; Environmental science; Carbon fibers; Flame speed; Materials science; Process engineering; Mechanics; Chemistry; Diffusion flame; Physics; Combustion; Engineering; Organic chemistry; Composite material","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":[0.0005138029,0.0005161379,0.0004738898,0.0003297674,0.0004759364,0.0006412242,0.0007547314,0.0006251107,0.0009988691],"category_scores_gemma":[0.001162192,0.0002835185,0.0005855681,0.0002807562,0.0003371605,0.0007473885,0.0003389486,0.0008359334,0.0002679537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005204352,"about_ca_system_score_gemma":0.0008390844,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00922455,"about_ca_topic_score_gemma":0.008056788,"domain_scores_codex":[0.9998739,0.00004483797,0.000007883366,0.0000183515,0.00004079702,0.00001431134],"domain_scores_gemma":[0.9996331,0.0002315117,0.00003620492,0.00002239157,0.00006572654,0.0000110355],"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":[0.00001497947,0.00002340332,0.0003227517,0.00002052101,0.0000120665,0.000007330207,0.00001414316,0.9855237,0.001133054,0.002590942,0.0001267374,0.01021043],"study_design_scores_gemma":[6.263595e-7,0.000002379596,0.00002394188,8.194158e-7,5.830039e-7,8.123725e-7,6.165811e-7,0.9992613,0.0002673776,0.0003612848,0.00007923524,9.178326e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04019085,0.0004022082,0.95603,0.0001015647,0.0000538149,0.00003869582,0.00005087702,0.0002774364,0.002854631],"genre_scores_gemma":[0.8231358,0.0005214,0.1668769,0.00005231749,0.00006886173,0.0001476373,0.0001654644,0.0001355841,0.008896098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00922455,"threshold_uncertainty_score":0.01834172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03741057027257844,"score_gpt":0.3124809590115697,"score_spread":0.2750703887389913,"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."}}