{"id":"W3090702224","doi":"10.1016/j.procs.2020.09.202","title":"Predictive analytics on open big data for supporting smart transportation services","year":2020,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":93,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"Big data; Computer science; Open data; Data science; Analytics; Predictive analytics; Public transport; Open government; Government (linguistics); Computer security; World Wide Web; Data mining; Transport engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002307586,0.001606702,0.0009351083,0.00392955,0.0008430338,0.003027013,0.001439449,0.0009182387,0.001605944],"category_scores_gemma":[0.01603851,0.0004080221,0.000926741,0.005646558,0.0009588252,0.004568525,0.002561961,0.002493547,0.0008256528],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001537882,"about_ca_system_score_gemma":0.001332248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02605701,"about_ca_topic_score_gemma":0.02621698,"domain_scores_codex":[0.9980968,0.0004748076,0.0001190469,0.0003259712,0.0008034561,0.0001798851],"domain_scores_gemma":[0.9928764,0.003715238,0.0005640346,0.001210979,0.001307588,0.0003258193],"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":[0.0005877843,0.0006353754,0.05601179,0.0008114285,0.0004786233,0.001596622,0.00115998,0.4670336,0.004964552,0.06711387,0.06776693,0.3318394],"study_design_scores_gemma":[0.00001546017,0.00003847729,0.004536928,0.0001202393,0.0000370511,0.0000807488,0.0006343809,0.9069194,0.001318598,0.07609275,0.01017302,0.00003287194],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2019756,0.007885542,0.693556,0.02248737,0.00163048,0.0008476155,0.03227276,0.01655796,0.02278674],"genre_scores_gemma":[0.8492364,0.003515748,0.120027,0.001166509,0.000663723,0.0002370253,0.02324686,0.0003679508,0.001538909],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02605701,"threshold_uncertainty_score":0.05181068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06075152541093842,"score_gpt":0.28085117084307,"score_spread":0.2200996454321316,"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."}}