{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00008906284,0.00006139894,0.00006393946,0.0001014314,0.00004549788,0.00001611471,0.00002286963,0.0000284392,7.505818e-7],"category_scores_gemma":[0.000009552768,0.00006432345,0.00001916079,0.0001353283,0.00001083332,0.0000155075,0.00001092654,0.00003290668,0.00001212687],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001479651,"about_ca_system_score_gemma":8.112672e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.30789e-7,"about_ca_topic_score_gemma":3.873791e-7,"domain_scores_codex":[0.9996803,0.000007641675,0.00006999062,0.00009005972,0.00003939181,0.0001126016],"domain_scores_gemma":[0.9998723,0.00003800022,0.000009647725,0.00004737338,0.000009732987,0.00002295935],"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":[0.00001114356,0.00001742565,0.0000560206,0.0002952306,0.00005175414,0.000003373463,0.001442307,0.5530097,0.0164487,0.0008647052,0.009342341,0.4184573],"study_design_scores_gemma":[0.0001595925,0.00001577418,0.0123657,0.00001624204,0.000007030879,7.858161e-7,0.00004846672,0.9791862,0.001263981,0.000002304324,0.006865813,0.00006808419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9515678,0.00000693307,0.04093862,0.00004597555,0.0002886378,0.0004357168,0.000009526643,0.00643909,0.0002676728],"genre_scores_gemma":[0.9996023,0.00002122732,0.0001752646,0.00001784235,0.00005561385,0.00002365391,0.00004004492,0.00001660763,0.0000474476],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4261765,"threshold_uncertainty_score":0.2623034,"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."}}