{"id":"W2461564529","doi":"10.1016/j.enconman.2016.06.064","title":"Combined effect of injection timing and exhaust gas recirculation (EGR) on performance and emissions of a DI diesel engine fuelled with next-generation advanced biofuel – diesel blends using response surface methodology","year":2016,"lang":"en","type":"article","venue":"Energy Conversion and Management","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":129,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Brake specific fuel consumption; Response surface methodology; NOx; Diesel fuel; Biofuel; Isobutanol; Exhaust gas recirculation; Diesel engine; Environmental science; Biodiesel; Pulp and paper industry; Exhaust gas; Combustion; Automotive engineering; Materials science; Waste management; Chemistry; Methanol; Engineering; Chromatography","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":[0.000275048,0.0001720153,0.0002587988,0.0002083914,0.00008123588,0.000005065562,0.00004845229,0.00007967243,0.000005837244],"category_scores_gemma":[0.0001180061,0.0001217971,0.00001958004,0.000188524,0.00009457349,0.000196686,0.0001059204,0.00006279591,1.358573e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005825323,"about_ca_system_score_gemma":0.000004931473,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007802908,"about_ca_topic_score_gemma":7.172995e-7,"domain_scores_codex":[0.9991874,0.00009066849,0.0002031069,0.0002581883,0.0001205936,0.0001401159],"domain_scores_gemma":[0.9992388,0.0003669577,0.0001300626,0.0001755167,0.00004558953,0.00004303282],"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":[0.002115008,0.00002065941,0.0005562098,0.0003139247,0.00007311119,0.000002228377,0.00009073489,0.09432808,0.8427954,0.001590996,0.000006722879,0.05810696],"study_design_scores_gemma":[0.003691611,0.001219248,0.001103837,0.0004607062,0.0000950968,0.000006648107,0.0002539119,0.3573923,0.6352106,0.0000567275,0.0002693643,0.000240006],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.886332,0.0001087423,0.1131151,0.0001215794,0.00005899425,0.0001346368,0.000001554509,0.0000827482,0.00004461697],"genre_scores_gemma":[0.9890891,0.001699074,0.008918389,0.000006982452,0.000006551918,0.00001129895,0.000005207844,0.00001632162,0.0002470561],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2630642,"threshold_uncertainty_score":0.496674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03877523890284275,"score_gpt":0.2584973394793955,"score_spread":0.2197221005765527,"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."}}