{"id":"W2553886687","doi":"10.1002/atr.1427","title":"Integrated model for traffic flow forecasting under rainy conditions","year":2016,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China","keywords":"Precipitation; Traffic flow (computer networking); Outlier; Computer science; Meteorology; Flow (mathematics); Quantitative precipitation forecast; Environmental science; Mathematics; Artificial intelligence; Geography","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":[0.0005225455,0.0006540477,0.0009058383,0.0005682478,0.0003198235,0.0007623676,0.0009705044,0.0007313723,0.001231589],"category_scores_gemma":[0.001192578,0.0003052403,0.0009991998,0.0006111442,0.0002103998,0.0008883228,0.0006228989,0.0009386105,0.0003236067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005849899,"about_ca_system_score_gemma":0.001028155,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02403811,"about_ca_topic_score_gemma":0.01321564,"domain_scores_codex":[0.9996487,0.0000490351,0.00002184061,0.00011649,0.0001079271,0.00005596258],"domain_scores_gemma":[0.9997151,0.00006827406,0.00004487773,0.00003081135,0.0001251629,0.00001564806],"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":[0.0000480784,0.00005003355,0.001977449,0.00001985513,0.00004321112,0.00003260502,0.00002492762,0.9698593,0.002160584,0.0007580212,0.0003859675,0.02463997],"study_design_scores_gemma":[0.000001532967,0.000007366843,0.0002402733,8.028815e-7,0.000004822257,0.000002685954,0.00000126564,0.9993196,0.0001655493,0.0001704981,0.00008348734,0.000002172836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1338535,0.0002185486,0.8612686,0.0001229736,0.0001100649,0.00007167018,0.0004326358,0.001384591,0.002537388],"genre_scores_gemma":[0.9545053,0.0001446975,0.04265045,0.00002905828,0.00003730887,0.0001080029,0.0004487747,0.00003714173,0.00203913],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02403811,"threshold_uncertainty_score":0.04779637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01976710060362305,"score_gpt":0.2406195956817513,"score_spread":0.2208524950781282,"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."}}