{"id":"W2995108027","doi":"10.18280/ria.330503","title":"An Automobile Noise Prediction Model Based on Extension Data Mining Algorithm","year":2019,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Vehicle Noise and Vibration Control","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Entropy (arrow of time); Noise (video); Data mining; Decision tree; Automotive industry; Artificial intelligence; Principle of maximum entropy; Autocorrelation; Logistic regression; Extension (predicate logic); Algorithm; Machine learning; Pattern recognition (psychology); Engineering; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003129238,0.0001692709,0.0001740279,0.0001180745,0.00008476283,0.00006964202,0.00037528,0.0001045541,0.0002721476],"category_scores_gemma":[0.00002396023,0.0001754708,0.00004407983,0.0002050161,0.00001763142,0.0004783045,0.0000330989,0.0001656347,0.0006161769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004162277,"about_ca_system_score_gemma":0.00003129609,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004751241,"about_ca_topic_score_gemma":0.000002488265,"domain_scores_codex":[0.9987383,0.00002978574,0.0003334626,0.0004519179,0.0001877758,0.0002587253],"domain_scores_gemma":[0.9984164,0.0000782894,0.00003988522,0.001298,0.00006029977,0.0001070946],"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":[0.00001052818,0.00006552818,0.00006936665,0.00002158808,0.000005072272,0.000002214341,0.0001237304,0.8739162,0.02304434,0.0000337469,0.00056998,0.1021377],"study_design_scores_gemma":[0.00007912468,0.0001390134,0.00004349637,0.00007388877,0.00001183184,0.000002349703,0.0001460777,0.976165,0.02224658,0.00003728601,0.000874857,0.0001804807],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08827383,0.00006069363,0.9080651,0.00007047712,0.0004941727,0.0003508856,0.0001272999,0.0004866051,0.002070908],"genre_scores_gemma":[0.9891083,0.00002148095,0.009956505,0.0001589228,0.0001507734,0.00002343402,0.0002328437,0.00004358159,0.0003041795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9008344,"threshold_uncertainty_score":0.7919912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03496262948036433,"score_gpt":0.265192592063113,"score_spread":0.2302299625827486,"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."}}