{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001244651,0.0008158261,0.0006728701,0.005997736,0.0006226983,0.002074557,0.001095836,0.0008211671,0.001085008],"category_scores_gemma":[0.005907747,0.0004536739,0.0007960966,0.004495682,0.0002443588,0.001945271,0.001109373,0.0008002586,0.001803427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003802468,"about_ca_system_score_gemma":0.0008957735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006221063,"about_ca_topic_score_gemma":0.009458266,"domain_scores_codex":[0.9988488,0.0002453452,0.0001272658,0.00025945,0.0004637957,0.00005528273],"domain_scores_gemma":[0.9951669,0.001847664,0.0005813925,0.001074319,0.001154558,0.0001751255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002844526,0.001198883,0.06805866,0.0005418888,0.0007541893,0.0009020186,0.0005001616,0.03643011,0.01813804,0.004085388,0.02259569,0.8465105],"study_design_scores_gemma":[0.0000538431,0.0001434034,0.03094009,0.0001438793,0.0001534433,0.001087821,0.0005378896,0.8923149,0.02538315,0.02429497,0.02485145,0.00009500054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1136322,0.0009263111,0.8204334,0.001102491,0.0001582458,0.001014249,0.008163433,0.04514451,0.00942509],"genre_scores_gemma":[0.4249375,0.0005979541,0.5558029,0.0002869888,0.0001184794,0.0004737451,0.01415585,0.0005951173,0.003031477],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006221063,"threshold_uncertainty_score":0.01236969,"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."}}