{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002830524,0.0001428992,0.0002613688,0.000146671,0.0000750697,0.000004154322,0.0002313328,0.00005932867,0.00003213363],"category_scores_gemma":[0.0004661096,0.0001628974,0.00008812913,0.0002739335,0.00001999665,0.00005112602,0.0002395806,0.0004181684,3.532435e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001356198,"about_ca_system_score_gemma":0.00001406151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000224501,"about_ca_topic_score_gemma":2.942352e-7,"domain_scores_codex":[0.9990902,0.00003081848,0.0002678457,0.0002136953,0.0001480892,0.0002493406],"domain_scores_gemma":[0.9994362,0.000158359,0.00009750216,0.0002301936,0.0000512018,0.00002652412],"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":[0.00001434139,0.00001177894,0.00001344419,0.00009220223,0.00001382853,0.000001166431,0.00007414156,0.6727896,0.3248541,0.0004970655,6.032998e-7,0.001637791],"study_design_scores_gemma":[0.0003288383,0.00004996036,7.045074e-7,0.00001768874,0.00002166767,0.000006347277,0.0003142114,0.924181,0.07199921,0.002568323,0.0003534773,0.0001585117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2226555,0.0005999312,0.7757102,0.00003397867,0.0001323955,0.0001464688,0.00001198626,0.0004631312,0.0002463668],"genre_scores_gemma":[0.7369599,0.000008985884,0.2627179,0.000005941059,0.00003004072,0.00002048078,0.00001215124,0.00004319257,0.0002014051],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5143044,"threshold_uncertainty_score":0.6642765,"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."}}