{"id":"W2397083313","doi":"","title":"Using Web Mining to Support Low Cost Historical Vehicle Traffic Analytics.","year":2014,"lang":"en","type":"article","venue":"Software Engineering and Knowledge Engineering","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Cluster analysis; Analytics; Computer science; Transport engineering; Web traffic; Web application; Web analytics; Data science; The Internet; World Wide Web; Engineering; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002910349,0.0004145678,0.0004034482,0.0005908314,0.00007641205,0.0000799502,0.0002100347,0.0001627463,0.000009075638],"category_scores_gemma":[0.0002158275,0.0004957934,0.00009690873,0.000539016,0.00001140789,0.0001708714,0.00009331133,0.0003068175,0.00002452848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004579614,"about_ca_system_score_gemma":0.00002052464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002322797,"about_ca_topic_score_gemma":0.000004769671,"domain_scores_codex":[0.9984651,0.00001103522,0.0003838777,0.0003680409,0.0001708689,0.0006011408],"domain_scores_gemma":[0.9991122,0.0001242066,0.00002227107,0.0002971793,0.00004412786,0.0003999438],"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":[0.000002988014,0.00002478362,0.0001764804,0.0004062213,0.00005605736,0.000007421819,0.0003259417,0.9537632,0.002326897,0.0001372924,0.006229888,0.03654275],"study_design_scores_gemma":[0.0002996413,0.00005302521,0.0002889773,0.0001689061,0.00003893712,0.00001481358,0.00001245341,0.8915338,0.0004913356,4.209936e-7,0.1065987,0.0004989773],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1893614,0.0003781348,0.7968047,0.00001440429,0.001605652,0.0002685081,0.000007669418,0.01128623,0.0002733268],"genre_scores_gemma":[0.9605297,0.0000441896,0.03869113,0.00001978684,0.0003789177,0.00005393189,0.000008307825,0.0001497065,0.0001243685],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7711683,"threshold_uncertainty_score":0.9997494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01486653354228461,"score_gpt":0.2208540104755533,"score_spread":0.2059874769332687,"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."}}