{"id":"W4229450111","doi":"10.3390/s22093592","title":"Dynamic Learning Framework for Smooth-Aided Machine-Learning-Based Backbone Traffic Forecasts","year":2022,"lang":"en","type":"article","venue":"Sensors","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Smoothing; Quality of service; Artificial intelligence; Exponential smoothing; Artificial neural network; Time series; Data mining; Deep learning; Process (computing); Machine learning; Real-time computing; 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":[0.0002133731,0.0002025599,0.000197767,0.0002357182,0.0003130156,0.00003449823,0.0001693303,0.00007977157,0.0001724562],"category_scores_gemma":[0.00007229146,0.0002391464,0.0001441232,0.0002802658,0.00002513788,0.00004237725,0.00004394728,0.0006984689,0.00001587783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001503196,"about_ca_system_score_gemma":0.00001058654,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004284174,"about_ca_topic_score_gemma":0.00001416795,"domain_scores_codex":[0.9988731,0.0000763795,0.0002287925,0.0002511797,0.0002175656,0.0003529646],"domain_scores_gemma":[0.9995297,0.000142131,0.00005435688,0.000182508,0.00001875091,0.00007257006],"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":[0.00003241651,0.00002680836,0.00003958847,0.00006387588,0.00003568505,0.000008980937,0.0001944951,0.9714624,0.0000869492,0.0004460061,0.002308965,0.0252938],"study_design_scores_gemma":[0.0003982256,0.0001610739,0.0001017358,0.00001648997,0.00002442603,0.000004096155,0.0002424424,0.875817,0.00007484031,0.00009311261,0.1228423,0.0002242213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4966603,0.0003471598,0.475905,0.0005963562,0.001170448,0.001152224,0.00006269039,0.0221823,0.001923567],"genre_scores_gemma":[0.9878148,0.00003261473,0.01094864,0.0000863069,0.00003466539,0.0002058493,0.0001339144,0.0001005871,0.0006426148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4911545,"threshold_uncertainty_score":0.9752108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008634188150996518,"score_gpt":0.2216526330960097,"score_spread":0.2130184449450132,"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."}}