{"id":"W2608497376","doi":"10.1155/2017/1738085","title":"Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data","year":2017,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Human Mobility and Location-Based Analysis","field":"Social Sciences","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Silhouette; Cluster analysis; License; Computer science; Homogeneous; Data mining; Artificial intelligence; Mathematics","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":[0.000314185,0.0006278086,0.000341609,0.005816611,0.0003561789,0.0006605989,0.000583448,0.0003522355,0.0008760564],"category_scores_gemma":[0.001211235,0.00019706,0.0006801446,0.003532952,0.0002239504,0.0005909365,0.0004167256,0.0003263331,0.0006789128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000890695,"about_ca_system_score_gemma":0.0008580012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05686437,"about_ca_topic_score_gemma":0.05299779,"domain_scores_codex":[0.9996081,0.00005281678,0.00003854324,0.0001353558,0.0001045734,0.00006061228],"domain_scores_gemma":[0.9993927,0.0001144401,0.0001284129,0.00007319009,0.0002469744,0.0000442371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003715522,0.0003636443,0.5619506,0.0003393429,0.0003109246,0.0006902976,0.001143157,0.1183622,0.017483,0.001676603,0.003861341,0.2934474],"study_design_scores_gemma":[0.000009693002,0.0001492731,0.4650466,0.00004344355,0.0001238385,0.0002402245,0.002788542,0.5144547,0.0124035,0.000743043,0.003883151,0.0001139925],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9517133,0.0001356463,0.04029721,0.0000930449,0.00002689398,0.0001547487,0.00481421,0.0004300859,0.002334837],"genre_scores_gemma":[0.9747903,0.000102501,0.01751007,0.000008773788,0.00001082984,0.00006167691,0.006281552,0.00001968712,0.00121456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05686437,"threshold_uncertainty_score":0.1130668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0844475274353989,"score_gpt":0.3611424207973806,"score_spread":0.2766948933619817,"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."}}