{"id":"W4252962063","doi":"10.1504/ijssca.2016.080449","title":"A DMAIC-based methodology for improving urban traffic quality with application for city of Montreal","year":2016,"lang":"en","type":"article","venue":"International Journal of Six Sigma and Competitive Advantage","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Traffic congestion; DMAIC; Transport engineering; Quality (philosophy); Descriptive statistics; Service quality; Computer science; Six Sigma; Engineering; Service (business); Business; Operations management; Statistics; Marketing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003516285,0.00008385111,0.0001770175,0.0001294403,0.00001735069,0.000008622577,0.0001301602,0.00003250914,0.00000364768],"category_scores_gemma":[0.00005577477,0.00005922225,0.00007280133,0.00002609411,0.00005392372,0.0001365994,0.00001034274,0.00004380998,7.206758e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004619268,"about_ca_system_score_gemma":0.00001836156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003910672,"about_ca_topic_score_gemma":0.00005210201,"domain_scores_codex":[0.9993711,0.00002685892,0.0002982944,0.00009209297,0.0001296946,0.00008192382],"domain_scores_gemma":[0.9989528,0.0004607251,0.0002020945,0.00005396677,0.0002942396,0.00003618257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003974895,0.0003540991,0.007057303,0.0003543159,0.0007959684,0.0000108137,0.0003809429,0.001965981,0.2211096,0.1586585,0.0005540541,0.6047835],"study_design_scores_gemma":[0.05889547,0.008960738,0.1290534,0.00184985,0.0009813888,0.0002437034,0.00475436,0.1196372,0.5568306,0.0130051,0.1036261,0.002161968],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1191987,0.00005246624,0.8798194,0.0002462564,0.0001202063,0.0002208806,0.0001063979,0.00008392722,0.000151749],"genre_scores_gemma":[0.9720812,0.00003373723,0.02767762,0.00004245026,0.00009614642,0.00003749415,0.000007226118,0.00001053305,0.00001357545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8528825,"threshold_uncertainty_score":0.2415013,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01810658649308825,"score_gpt":0.2955345917638035,"score_spread":0.2774280052707152,"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."}}