{"id":"W2799435900","doi":"10.1007/s41870-018-0171-7","title":"A fine tuned tracking of vehicles under different video degradations","year":2018,"lang":"en","type":"article","venue":"International Journal of Information Technology","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Kalman filter; Noise (video); Tracking (education); Computer vision; Real-time computing; sort; Artificial intelligence; Tracking system; Vehicle tracking system; Simulation","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":[],"consensus_categories":[],"category_scores_codex":[0.00009893961,0.00006940019,0.0001108097,0.0009739647,0.00002030906,0.00002463904,0.0003185148,0.00007591182,0.00003801765],"category_scores_gemma":[0.00006263409,0.00006156181,0.00005021174,0.0001654389,0.00007443723,0.0006457531,0.00003599796,0.0001279903,0.00001030834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000697126,"about_ca_system_score_gemma":0.00001285454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001335775,"about_ca_topic_score_gemma":0.000007947945,"domain_scores_codex":[0.9991368,0.000005301733,0.0005368525,0.0000275457,0.0002206837,0.00007278834],"domain_scores_gemma":[0.9991554,0.00001938577,0.0002361085,0.00007845619,0.0004905194,0.00002011258],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00008650918,0.0001261569,0.00187601,0.00005008869,0.0006855513,0.000004566422,0.0007362681,0.003188661,0.02332448,0.1587359,0.03463734,0.7765485],"study_design_scores_gemma":[0.003720998,0.0008533573,0.03565225,0.0005525453,0.0001098749,0.000425833,0.001870933,0.05821462,0.766889,0.02704293,0.1041542,0.0005134547],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2742029,0.00003795619,0.7210508,0.001432031,0.0007956608,0.00006425114,0.000009835684,0.0006046237,0.00180196],"genre_scores_gemma":[0.9962276,0.00007215449,0.003539501,0.00006803783,0.00006955068,0.000003413984,0.000007357232,0.000004876335,0.000007457713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.776035,"threshold_uncertainty_score":0.2510418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007822642153568116,"score_gpt":0.2346354002666033,"score_spread":0.2268127581130352,"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."}}