{"id":"W3081229622","doi":"","title":"Use of Artificial Intelligence for Injection Control Systems","year":2020,"lang":"en","type":"article","venue":"ICTEA: International Conference on Thermal Engineering","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Fuel injection; Automotive engineering; Diesel fuel; Fuel efficiency; Transient (computer programming); Torque; Water injection (oil production); Diesel engine; Engineering; Control system; Duration (music); Computer science; Control (management); Control engineering; Control theory (sociology); Electrical engineering; Artificial intelligence; Petroleum engineering; Physics","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":[0.001198569,0.0008370433,0.0006323581,0.0006501894,0.0003310362,0.001877479,0.0008330127,0.0008862557,0.001545194],"category_scores_gemma":[0.002862233,0.0002771719,0.0005877796,0.0005335024,0.001260579,0.001314485,0.0008538532,0.001504779,0.0005127415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005840353,"about_ca_system_score_gemma":0.0004627182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009032338,"about_ca_topic_score_gemma":0.0005705522,"domain_scores_codex":[0.9989555,0.0003926436,0.00007690627,0.0001510581,0.0003808453,0.00004303654],"domain_scores_gemma":[0.9986091,0.0009373113,0.00008513773,0.0001583123,0.0001886672,0.00002150953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002048673,0.0001661462,0.001542847,0.001318757,0.0003757906,0.0004430169,0.0003842094,0.300751,0.0197275,0.2168673,0.003005262,0.4552132],"study_design_scores_gemma":[0.00004478881,0.0002078649,0.0005663975,0.000178147,0.00009000788,0.0002545863,0.00005367281,0.845126,0.01221168,0.1042687,0.03693848,0.00005973592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008463786,0.004344527,0.9684448,0.000735093,0.0001768352,0.00008753825,0.00003551568,0.000881211,0.01683054],"genre_scores_gemma":[0.4962793,0.005654346,0.4913573,0.0004936043,0.0003160406,0.0002604146,0.0001128223,0.0001050758,0.005421191],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001877479,"threshold_uncertainty_score":0.006338775,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08458698287114319,"score_gpt":0.2760681666837325,"score_spread":0.1914811838125893,"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."}}