{"id":"W2091826738","doi":"10.3141/1856-10","title":"GAID: Genetic Adaptive Incident Detection for Freeways","year":2003,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of California, Irvine; Ministère des Transports; McMaster University","keywords":"Computer science; Genetic algorithm; Constant false alarm rate; Artificial neural network; Detector; Probabilistic logic; Algorithm; Smoothing; Data mining; Artificial intelligence; Real-time computing; Pattern recognition (psychology); Machine learning; Computer vision","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004649003,0.000718836,0.0004741093,0.0007069623,0.0003612124,0.0006512561,0.001492866,0.0006750121,0.001667732],"category_scores_gemma":[0.001895886,0.0003376971,0.0005658732,0.0004741887,0.0004883431,0.0004978962,0.0006633878,0.00107001,0.0003341878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001346503,"about_ca_system_score_gemma":0.001240279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01584389,"about_ca_topic_score_gemma":0.01268156,"domain_scores_codex":[0.9996878,0.00004799729,0.00001086071,0.00009424734,0.0001281989,0.00003096023],"domain_scores_gemma":[0.9995275,0.0002347717,0.0000689252,0.00004077223,0.0001025822,0.00002559816],"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":[0.00008684928,0.00008305081,0.002928499,0.00005408261,0.00005699594,0.000066564,0.00007196434,0.7357531,0.005616322,0.007066449,0.002251954,0.2459642],"study_design_scores_gemma":[0.00001109778,0.0000239247,0.0003136712,0.000003461681,0.000005950282,0.0000168197,0.000005000434,0.9952887,0.001780087,0.001622849,0.0009215042,0.000007026776],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02260255,0.00007582604,0.9716074,0.00006813616,0.00002924135,0.00006975576,0.00008415378,0.003882062,0.001580763],"genre_scores_gemma":[0.288052,0.00008979652,0.7073138,0.0001202676,0.00001530211,0.0002052031,0.0003507815,0.0002403242,0.003612486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01584389,"threshold_uncertainty_score":0.03150338,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06407185985341876,"score_gpt":0.3311411520460345,"score_spread":0.2670692921926158,"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."}}