{"id":"W3110006947","doi":"10.1155/2020/8870211","title":"A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Korea Institute for Advancement of Technology; Ministry of Trade, Industry and Energy","keywords":"Occupancy; Computer science; Process (computing); Toll; Binary number; Artificial intelligence; Face (sociological concept); Computer vision; Real-time computing; Engineering; Mathematics","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":[0.001075401,0.000959751,0.0009552917,0.001874503,0.0005999111,0.001254816,0.002362282,0.001192429,0.001616898],"category_scores_gemma":[0.002003421,0.0004611465,0.00128798,0.001106532,0.0008175973,0.001465475,0.001062778,0.001040296,0.0006802914],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001138738,"about_ca_system_score_gemma":0.001739801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02018031,"about_ca_topic_score_gemma":0.01214021,"domain_scores_codex":[0.9989201,0.0002072332,0.0000566797,0.0003737601,0.0003107956,0.0001314739],"domain_scores_gemma":[0.9993991,0.0001775119,0.00008614535,0.00006335855,0.0002272903,0.00004656456],"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":[0.0002032192,0.0002348873,0.008146439,0.0003131145,0.0001654788,0.0005462557,0.0005234853,0.4687588,0.01317087,0.05977204,0.005476333,0.442689],"study_design_scores_gemma":[0.000007319731,0.00003652498,0.0005789393,0.00001521363,0.00002480852,0.0001038167,0.00003128277,0.9876364,0.00173645,0.007119656,0.002686332,0.00002327158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003087829,0.000187281,0.9953956,0.00006734533,0.00002752589,0.00004043659,0.00004611534,0.0003674002,0.0007803554],"genre_scores_gemma":[0.2695218,0.0007284215,0.7244357,0.0001653102,0.0001557422,0.0003667403,0.0005228292,0.0001235988,0.00397975],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02018031,"threshold_uncertainty_score":0.04012573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04340077653075523,"score_gpt":0.3335237510633671,"score_spread":0.2901229745326119,"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."}}