{"id":"W2520770428","doi":"10.4271/2016-01-2311","title":"Experimental Analysis of Combustion Noise Reduction with Performance Optimization in 110cc CVT Scooter Engine","year":2016,"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":"","funders":"Banting and Best Diabetes Centre, University of Toronto","keywords":"Automotive engineering; Noise reduction; Reduction (mathematics); Combustion; Noise (video); Computer science; Environmental science; Engineering; Chemistry; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002900871,0.0006349203,0.000966401,0.000903349,0.0001077458,0.00002200538,0.000646668,0.0006209465,0.0003128916],"category_scores_gemma":[0.0003867514,0.0004685845,0.0002736995,0.002591283,0.000731901,0.000819745,0.0002320664,0.0007189597,0.00001657146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006310612,"about_ca_system_score_gemma":0.00003678974,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001099923,"about_ca_topic_score_gemma":0.0007460763,"domain_scores_codex":[0.9965824,0.00005473689,0.001043691,0.0009433307,0.000703951,0.0006719005],"domain_scores_gemma":[0.9980174,0.0002280437,0.0002933296,0.001162316,0.0001407633,0.0001581455],"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":[0.000416539,0.0002815232,0.00023233,0.0000296875,0.00009762525,0.000006803779,0.00001446607,0.2228415,0.7717943,0.001900237,0.00003444199,0.002350631],"study_design_scores_gemma":[0.002990224,0.002175337,0.914362,0.001007302,0.0005098361,0.0000665428,0.0002620835,0.0003422367,0.07597506,0.0001724168,0.0006551879,0.001481835],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9897151,0.0001944361,0.001291375,0.001261138,0.0001229714,0.0007454417,0.00003818094,0.0031496,0.003481773],"genre_scores_gemma":[0.9787558,0.0002337708,0.02027475,0.0000790102,0.00004361744,0.0002809892,0.00005516168,0.0001002543,0.0001766154],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9141296,"threshold_uncertainty_score":0.9997766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00814553118209723,"score_gpt":0.2297832473195869,"score_spread":0.2216377161374896,"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."}}