{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003436713,0.0005278625,0.0004094173,0.0005218842,0.0003453686,0.0006495719,0.001152067,0.0005959149,0.00116855],"category_scores_gemma":[0.001165502,0.0002669679,0.0004404943,0.0004014565,0.0004032839,0.0005102353,0.0004830279,0.0007781416,0.000269287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001064772,"about_ca_system_score_gemma":0.0009852119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0122145,"about_ca_topic_score_gemma":0.01258629,"domain_scores_codex":[0.9997337,0.00003528254,0.00001107841,0.00008252841,0.0001060544,0.0000313152],"domain_scores_gemma":[0.9997409,0.00009422484,0.00003468587,0.00003021843,0.00008275875,0.00001722316],"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":[0.00008817053,0.00009668431,0.003149707,0.00005653298,0.00007696254,0.00008095984,0.00008594115,0.6010507,0.008052191,0.01153786,0.002500717,0.3732235],"study_design_scores_gemma":[0.00001414735,0.00003829534,0.0004393853,0.000004566897,0.00001262124,0.00003076748,0.000007659727,0.9903189,0.004076577,0.002519257,0.00252705,0.00001078634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01679251,0.00008761186,0.9791621,0.00005987655,0.00004149925,0.00005940996,0.00005763287,0.001872408,0.001866932],"genre_scores_gemma":[0.3003358,0.0001118364,0.69309,0.0001472871,0.00001993466,0.0001645795,0.0002753354,0.0001431718,0.005712174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0122145,"threshold_uncertainty_score":0.02428681,"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."}}