{"id":"W2338446151","doi":"","title":"Real-time Traffic Flow Prediction using Augmented Reality","year":2016,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Traffic flow (computer networking); Data mining; Chaotic; Flow (mathematics); Traffic congestion; Scale (ratio); Variation (astronomy); Real-time computing; Artificial intelligence; Transport engineering; Engineering; Computer network","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":[0.0004474006,0.000223565,0.0002728725,0.0003284912,0.0002577675,0.00001772868,0.0003586582,0.0002325022,0.0004137811],"category_scores_gemma":[0.00002195976,0.0002376886,0.0001636945,0.0003414424,0.0001415797,0.0008533944,0.0001248373,0.0001768175,0.0001195137],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003997606,"about_ca_system_score_gemma":0.0000260842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002991035,"about_ca_topic_score_gemma":0.00004626361,"domain_scores_codex":[0.9986254,0.00009879896,0.0002291554,0.0003405497,0.0003577006,0.0003483856],"domain_scores_gemma":[0.9991615,0.00004181885,0.00009346398,0.0004476953,0.00008077806,0.0001747905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00057832,0.0003394043,0.01085932,0.0003598672,0.0007909845,0.00008547404,0.001489226,0.01938706,0.8331966,0.0003583587,0.04779064,0.08476479],"study_design_scores_gemma":[0.01134823,0.0006459855,0.4914684,0.001514637,0.001004559,0.00008366583,0.001103505,0.405958,0.03407186,0.000360991,0.04997437,0.002465795],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.97536,0.00002230295,0.01486642,0.0001927131,0.0002546745,0.0002976737,0.0002000617,0.004319926,0.004486215],"genre_scores_gemma":[0.9952326,0.0002162844,0.00334237,0.000009957628,0.0000590809,7.137714e-7,0.00003903599,0.00003731917,0.001062598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7991247,"threshold_uncertainty_score":0.9692661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01753305482131435,"score_gpt":0.2055905796399529,"score_spread":0.1880575248186385,"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."}}