{"id":"W3168387826","doi":"10.1155/2021/6687378","title":"Driving Style Recognition under Connected Circumstance Using a Supervised Hierarchical Bayesian Model","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Latent Dirichlet allocation; Computer science; Artificial intelligence; Software deployment; Machine learning; Intelligent transportation system; Bayesian probability; Engineering; Topic model","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000621884,0.0006366349,0.0006820828,0.0008535949,0.0003778367,0.0005710075,0.001073254,0.0006072556,0.001405746],"category_scores_gemma":[0.001652611,0.0003835509,0.001050011,0.0006469486,0.000361497,0.0007918141,0.000540415,0.000888126,0.0006859463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005419615,"about_ca_system_score_gemma":0.0006786466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01482803,"about_ca_topic_score_gemma":0.02368088,"domain_scores_codex":[0.9994991,0.0001189772,0.00002476081,0.0002032893,0.00007285274,0.000081092],"domain_scores_gemma":[0.999376,0.0002606179,0.00008184111,0.00007525821,0.000157403,0.00004894166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007467703,0.0006501968,0.04010271,0.000196766,0.0003110303,0.0002648733,0.0007614421,0.4465522,0.01149754,0.01382051,0.007280453,0.4778155],"study_design_scores_gemma":[0.000007496018,0.00001889413,0.002345118,0.000004749284,0.0000128468,0.00001981541,0.00001678815,0.9941553,0.0003813536,0.002725803,0.0003014264,0.00001044397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2433407,0.0004823851,0.7503629,0.000383646,0.0000744062,0.0001018767,0.0008091136,0.0007885614,0.003656511],"genre_scores_gemma":[0.9446285,0.0002231524,0.04923793,0.0001171446,0.00008483452,0.00009747162,0.00149984,0.00005963597,0.004051519],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01482803,"threshold_uncertainty_score":0.02948344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0141040184285534,"score_gpt":0.2302910251917323,"score_spread":0.2161870067631789,"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."}}