{"id":"W2087113501","doi":"10.1016/j.apenergy.2011.11.014","title":"Numerical study of a homogeneous charge compression ignition (HCCI) engine fueled with biogas","year":2011,"lang":"en","type":"article","venue":"Applied Energy","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor; University of New Brunswick; University of Toronto; University of Waterloo","funders":"Banting and Best Diabetes Centre, University of Toronto","keywords":"Homogeneous charge compression ignition; Automotive engineering; Ignition system; Compression ratio; Calibration; Diesel fuel; Diesel engine; Nuclear engineering; Combustion; Computer science; Internal combustion engine; Simulation; Materials science; Environmental science; Combustion chamber; Engineering; Chemistry; Thermodynamics; Mathematics; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0000266666,0.0002126383,0.0002894008,0.0001347214,0.00003668576,0.00000293112,0.000229067,0.0001001237,0.0001179897],"category_scores_gemma":[0.00001131612,0.0001775876,0.0000307239,0.000359548,0.00003951219,0.00004480128,0.00007320141,0.0001522228,0.00001213064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003257431,"about_ca_system_score_gemma":0.000006199732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007573696,"about_ca_topic_score_gemma":0.000002573617,"domain_scores_codex":[0.9990289,0.000008251751,0.0002448289,0.0002754866,0.0001940937,0.0002484933],"domain_scores_gemma":[0.9993719,0.00003557705,0.00009757067,0.0003895016,0.00004702948,0.00005835931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008494651,0.00380933,0.0002233717,0.00007956649,0.0003745198,0.00008696492,0.001344419,0.3352785,0.5146809,0.1127047,0.0001255778,0.03044268],"study_design_scores_gemma":[0.002298054,0.0006616035,0.0001503319,0.00003322787,0.00005831562,0.00001631525,0.0005114726,0.02095977,0.9733323,0.0009791631,0.0005528159,0.0004465727],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5040022,0.00007314565,0.4861184,0.000008900698,0.00005835787,0.0002480658,0.000005031618,0.001437474,0.008048434],"genre_scores_gemma":[0.9921342,0.0000103021,0.007559333,0.00001126733,0.00002700787,0.0001463284,0.00001648928,0.00004595597,0.00004914391],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.488132,"threshold_uncertainty_score":0.7241811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0168060701166645,"score_gpt":0.2047498426758384,"score_spread":0.1879437725591739,"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."}}