{"id":"W2918458045","doi":"","title":"iCity: big data and visualization urban transportation strategies","year":2018,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Esri (Canada); University of Saskatchewan; University of Toronto","funders":"","keywords":"Transportation planning; Computer science; Big data; Sustainable transport; Visualization; Urban planning; Transport engineering; Intelligent transportation system; Data science; Engineering; Sustainability; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.0009750476,0.001146646,0.0007304546,0.00349621,0.0007300312,0.004623721,0.0013645,0.001203236,0.0154564],"category_scores_gemma":[0.004245547,0.000526519,0.001091686,0.003959168,0.0005890707,0.002856784,0.002546662,0.001534339,0.002836637],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009485603,"about_ca_system_score_gemma":0.001217777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01193725,"about_ca_topic_score_gemma":0.01403124,"domain_scores_codex":[0.9995338,0.0001184165,0.00004649628,0.0000809325,0.0001793049,0.00004112308],"domain_scores_gemma":[0.998578,0.0005726136,0.00009827429,0.0003138548,0.0003095924,0.0001276197],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003165616,0.0002719398,0.01153057,0.00135846,0.0004512779,0.0006884588,0.001238445,0.1579684,0.005842119,0.06664892,0.4269314,0.3267535],"study_design_scores_gemma":[0.00006486378,0.00004832538,0.007160999,0.000280033,0.00006351639,0.0002175282,0.0009774818,0.7106346,0.004292464,0.09361792,0.1825286,0.0001135114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04796663,0.006889457,0.7035807,0.01424321,0.002685308,0.0007689268,0.08556271,0.08156919,0.05673389],"genre_scores_gemma":[0.5189828,0.006901368,0.3889138,0.001541869,0.0007606173,0.001221649,0.06269556,0.005575715,0.01340665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0154564,"threshold_uncertainty_score":0.05170685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01794465574664724,"score_gpt":0.2278201609880907,"score_spread":0.2098755052414435,"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."}}