{"id":"W3175595612","doi":"10.23977/acss.2021.050103","title":"Traffic congestion index calculation based on BP neural network","year":2021,"lang":"en","type":"article","venue":"Advances in Computer Signals and Systems","topic":"Industrial Technology and Control Systems","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Computer science; Visibility; Traffic congestion; Data mining; Regularization (linguistics); Index (typography); Network traffic control; Artificial intelligence; Computer network; Engineering; Geography; Transport engineering; Meteorology","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":[0.000649671,0.0009000529,0.0007916669,0.001989576,0.0004350681,0.001008012,0.0007956279,0.0006363692,0.001041064],"category_scores_gemma":[0.001844258,0.0003592377,0.000480553,0.001717717,0.0002627233,0.001334117,0.0005192002,0.0007452392,0.0002525857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007323124,"about_ca_system_score_gemma":0.0007272591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01287398,"about_ca_topic_score_gemma":0.00524354,"domain_scores_codex":[0.9993517,0.00008379905,0.00005149851,0.0001393217,0.0002996721,0.00007414306],"domain_scores_gemma":[0.999569,0.00009154539,0.00004227535,0.00001801575,0.0002582147,0.00002092828],"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":[0.000348012,0.0003149054,0.01315937,0.0003151682,0.0001968199,0.0002392641,0.0001457273,0.6488389,0.01689475,0.003748954,0.004200161,0.3115979],"study_design_scores_gemma":[0.000008205731,0.00002608271,0.002024983,0.00000639041,0.00001594395,0.00001941762,0.00001149505,0.9949815,0.002031288,0.0005827904,0.0002790348,0.00001291829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.181951,0.0009021444,0.8060588,0.0002551631,0.0002779324,0.0002028301,0.0003159304,0.002356172,0.007679984],"genre_scores_gemma":[0.9261145,0.0006883817,0.06912536,0.00004518512,0.00007675048,0.0002575827,0.0004708833,0.00006782656,0.003153624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01287398,"threshold_uncertainty_score":0.02559811,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142014799992883,"score_gpt":0.2218623903000368,"score_spread":0.2104422423001079,"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."}}