{"id":"W3039500187","doi":"10.4271/2020-01-2017","title":"A Machine Learning Modeling Approach for High Pressure Direct Injection Dual Fuel Compressed Natural Gas Engines","year":2020,"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":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Compressed natural gas; Dual (grammatical number); Natural gas; Computer science; Automotive engineering; Natural (archaeology); Mechanical engineering; Engineering; Waste management; Geology","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":[0.0005026205,0.0006738619,0.0005581452,0.0004452231,0.0003622558,0.0006643077,0.0007171531,0.0005806363,0.001091639],"category_scores_gemma":[0.00103153,0.0003307775,0.0006839028,0.0004078374,0.0002593177,0.0004827147,0.0003628946,0.0006947569,0.0002469358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000854688,"about_ca_system_score_gemma":0.0008672854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01694384,"about_ca_topic_score_gemma":0.01191676,"domain_scores_codex":[0.9997672,0.00006861103,0.00001223268,0.00005230674,0.00008615325,0.0000134378],"domain_scores_gemma":[0.9997304,0.000161407,0.00003047888,0.00001554872,0.00005586456,0.000006199783],"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":[0.00001511116,0.00003873049,0.0003076522,0.00002191694,0.00001250575,0.00001758732,0.00001791106,0.9831188,0.002027075,0.001557997,0.0001077791,0.01275691],"study_design_scores_gemma":[8.772294e-7,0.000006543697,0.0001069803,8.88171e-7,0.000001449653,0.000002165708,0.000001868008,0.9991055,0.0003376874,0.0003035759,0.0001304936,0.000001982781],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05578364,0.0003345425,0.9386734,0.0002163407,0.00001431575,0.00008037938,0.000190864,0.0004010744,0.004305372],"genre_scores_gemma":[0.8284046,0.0006712076,0.1614728,0.0000685855,0.00002802733,0.0004463968,0.0005229784,0.00007562451,0.008309848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01694384,"threshold_uncertainty_score":0.03369045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0148388092903663,"score_gpt":0.229620541355784,"score_spread":0.2147817320654177,"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."}}