{"id":"W4388972011","doi":"10.3390/s23239385","title":"Edge Computing for Effective and Efficient Traffic Characterization","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Higher Education Commission Mauritius; Higher Education Commission, Pakistan","keywords":"Headway; Edge computing; Computer science; Cloud computing; Real-time computing; Enhanced Data Rates for GSM Evolution; Node (physics); Centroid; Bandwidth (computing); Traffic flow (computer networking); Simulation; Engineering; Computer network; Artificial intelligence","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":[0.0001210474,0.0005198321,0.0003500334,0.0008764305,0.0002991719,0.0007788545,0.0006076504,0.0002723179,0.002652207],"category_scores_gemma":[0.0005737304,0.0001580855,0.0002365152,0.001139337,0.0001584892,0.001164007,0.0004944807,0.0003604897,0.0007516175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002817317,"about_ca_system_score_gemma":0.0002765004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002664201,"about_ca_topic_score_gemma":0.003307155,"domain_scores_codex":[0.9998614,0.00001635285,0.000007363586,0.00003282859,0.00005523685,0.00002688468],"domain_scores_gemma":[0.9998498,0.0000372489,0.00001668868,0.00002890851,0.00005807573,0.000009301105],"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":[0.0003742832,0.0001913463,0.006484431,0.0002097937,0.00005911959,0.0002250462,0.0001377961,0.1655764,0.04199158,0.02252493,0.02080806,0.7414171],"study_design_scores_gemma":[0.00001112578,0.00006178518,0.002492442,0.00002647493,0.00002128714,0.0001147425,0.00009661387,0.9478822,0.01739755,0.01629239,0.01557452,0.00002888821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09008393,0.0009780966,0.8821343,0.0003993569,0.0002185445,0.0001089286,0.0009825102,0.005818448,0.01927589],"genre_scores_gemma":[0.8151309,0.001096901,0.175952,0.0001856079,0.0001130944,0.00008668139,0.001622821,0.0002690295,0.005542955],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002664201,"threshold_uncertainty_score":0.008872509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006353222316050135,"score_gpt":0.2143572480958819,"score_spread":0.2080040257798318,"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."}}