{"id":"W2053863336","doi":"10.1111/j.1467-8667.2005.00390","title":"Mobile Active-Vision Traffic Surveillance System for Urban Networks","year":2005,"lang":"en","type":"article","venue":"Computer-Aided Civil and Infrastructure Engineering","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"auDA Foundation","keywords":"Active vision; Computer science; Focus (optics); Machine vision; Zoom; Artificial intelligence; Computer vision; Smart camera; Orientation (vector space); Task (project management); Guidance system; Real-time computing; Human–computer interaction; Engineering; Systems engineering","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":[0.0003635979,0.0003285003,0.0005201418,0.001089617,0.0005161311,0.0005052718,0.0006219993,0.0005805171,0.002356679],"category_scores_gemma":[0.0004900065,0.0001894806,0.0002008708,0.0004311204,0.0001195476,0.0004436945,0.0003417084,0.0003706204,0.0008983231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000477265,"about_ca_system_score_gemma":0.0004868377,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003256903,"about_ca_topic_score_gemma":0.005773312,"domain_scores_codex":[0.999822,0.00003012305,0.000004999933,0.00004709138,0.00006393778,0.00003199462],"domain_scores_gemma":[0.9995348,0.00004988528,0.00003358729,0.00004086993,0.0002799436,0.0000608901],"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":[0.002641138,0.0008958677,0.02613203,0.0001897523,0.0001704271,0.0004187429,0.0003570705,0.01771654,0.338427,0.002695668,0.02172143,0.5886344],"study_design_scores_gemma":[0.0003141862,0.00138057,0.04042877,0.00003608401,0.0003716554,0.0009566492,0.0001689788,0.848415,0.0884433,0.001434227,0.01797507,0.00007561143],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5857094,0.0006577976,0.3775312,0.0004579724,0.0002756194,0.0003619598,0.001183954,0.01257553,0.02124665],"genre_scores_gemma":[0.941186,0.0001302989,0.05142757,0.00018608,0.00009198638,0.00009458395,0.0007769082,0.00007801901,0.00602858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003256903,"threshold_uncertainty_score":0.007883847,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002841458261355189,"score_gpt":0.2027537805741919,"score_spread":0.1999123223128367,"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."}}