{"id":"W2809923164","doi":"10.1155/2018/5942763","title":"Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Scholarship Council","keywords":"Probabilistic logic; Computer science; Term (time); Inflow; Autoregressive model; Schedule; Selection (genetic algorithm); Predictive modelling; Data mining; Decision tree; Machine learning; Artificial intelligence","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":[0.0009151188,0.0007660559,0.001082735,0.0008617024,0.0003831451,0.0005175191,0.001121268,0.0006962576,0.0009386033],"category_scores_gemma":[0.00125779,0.0004925578,0.0008919176,0.0008141327,0.0002041424,0.0009035328,0.0004058195,0.0007170588,0.0002099171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004651592,"about_ca_system_score_gemma":0.0007779514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01664153,"about_ca_topic_score_gemma":0.01140576,"domain_scores_codex":[0.999663,0.0001044575,0.00001858553,0.00009027808,0.00006972072,0.00005396079],"domain_scores_gemma":[0.9993901,0.0003351976,0.00008126812,0.00003146634,0.0001277759,0.00003414515],"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":[0.00004412918,0.00004603454,0.002698762,0.00001811124,0.00005223022,0.00004145502,0.00001542716,0.9731843,0.000479529,0.0008287209,0.0004434937,0.02214779],"study_design_scores_gemma":[9.520633e-7,0.000002992952,0.000129399,4.274449e-7,0.000002881545,0.00000175717,0.000001108019,0.9996351,0.00003926737,0.0001644157,0.00002034475,0.000001244093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1303469,0.0004538131,0.8664466,0.0002986411,0.00005900587,0.00004782139,0.000311992,0.0007652065,0.001269956],"genre_scores_gemma":[0.9548679,0.000254024,0.04292487,0.00004808626,0.00007463928,0.00009587347,0.0005441796,0.0000292208,0.001161173],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01664153,"threshold_uncertainty_score":0.03308934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01119682542490998,"score_gpt":0.2269209155078407,"score_spread":0.2157240900829307,"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."}}