{"id":"W2577075991","doi":"","title":"GAID, GENETIC ADAPTIVE INCIDENT DETECTION FOR FREEWAYS","year":2003,"lang":"en","type":"article","venue":"Transportation Research Board 82nd Annual MeetingTransportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Constant false alarm rate; Genetic algorithm; Artificial neural network; Probabilistic logic; Smoothing; Detector; Artificial intelligence; Data mining; Generalization; Pattern recognition (psychology); Algorithm; Machine learning; Mathematics; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.004058729,0.0004408458,0.0004369792,0.001563845,0.0008487548,0.0001755894,0.0005213554,0.0003687226,0.0001698927],"category_scores_gemma":[0.00027414,0.0004904743,0.0002678267,0.001835045,0.0004330734,0.0006796813,0.000007467915,0.001299722,0.00008678216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003586225,"about_ca_system_score_gemma":0.0001795686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009472481,"about_ca_topic_score_gemma":0.00878896,"domain_scores_codex":[0.9932766,0.0005935891,0.001069021,0.0009035217,0.002555283,0.001601984],"domain_scores_gemma":[0.9959197,0.000680063,0.0001007304,0.0005380778,0.002226519,0.0005349209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.004211739,0.002202448,0.0282339,0.0060888,0.001886926,0.0006160401,0.0346818,0.1811528,0.1108454,0.1456856,0.3131995,0.171195],"study_design_scores_gemma":[0.007938132,0.003883433,0.386902,0.0007135893,0.0002507332,0.000003962481,0.02718897,0.04071398,0.1213244,0.01104354,0.3972541,0.002783201],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5778154,0.0005947993,0.4014344,0.0002877763,0.0007231035,0.005393628,0.0006058563,0.00478948,0.008355608],"genre_scores_gemma":[0.9850492,0.0007759605,0.01072959,0.00003999872,0.0001774454,0.002354931,0.0001777976,0.0001627362,0.0005322706],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.407234,"threshold_uncertainty_score":0.9997547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04717613262616813,"score_gpt":0.3293827751223179,"score_spread":0.2822066424961497,"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."}}