{"id":"W4296825027","doi":"10.4271/2022-01-5075","title":"Refinement of Gaussian Process Regression Modeling of Pilot-Ignited Direct-Injected Natural Gas Engines","year":2022,"lang":"en","type":"article","venue":"SAE technical papers on CD-ROM/SAE technical paper series","topic":"Advanced Combustion Engine Technologies","field":"Chemical Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Kriging; Natural gas; Gaussian process; Process (computing); Computer science; Regression analysis; Regression; Data modeling; Gaussian; Statistics; Engineering; Machine learning; Mathematics; Physics; Waste management; Software engineering; Programming language","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004719994,0.0008690784,0.001335803,0.0006127144,0.0003197998,0.00001863939,0.001595611,0.0004330298,0.0004065955],"category_scores_gemma":[0.001764859,0.0007553152,0.0004068309,0.002208584,0.0005509868,0.0003387901,0.001017481,0.002196116,0.000006352661],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004448964,"about_ca_system_score_gemma":0.00009159298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003073994,"about_ca_topic_score_gemma":0.0009925823,"domain_scores_codex":[0.9947182,0.0001149889,0.001665784,0.001132535,0.001440492,0.0009280026],"domain_scores_gemma":[0.9970167,0.0004224373,0.000525527,0.001560607,0.0002730201,0.0002016714],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0006675659,0.0005357436,0.00002820352,0.0002199289,0.00006851935,0.00002256328,0.00003803708,0.06930804,0.9209855,0.005896024,0.0002351296,0.0019948],"study_design_scores_gemma":[0.02694038,0.03812865,0.560206,0.01177302,0.002290776,0.001242977,0.01040792,0.007276544,0.2385543,0.04592023,0.03812723,0.01913193],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9636116,0.001687791,0.00009729135,0.002656998,0.0004977648,0.001804506,0.0002272856,0.01070445,0.01871229],"genre_scores_gemma":[0.9913502,0.0001798697,0.007132754,0.0001405712,0.00005688644,0.0006034642,0.0001233113,0.0001754043,0.0002375169],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6824311,"threshold_uncertainty_score":0.9994898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01404506370366855,"score_gpt":0.255365839319784,"score_spread":0.2413207756161155,"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."}}