{"id":"W3197367263","doi":"10.3390/fi13090225","title":"Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling","year":2021,"lang":"en","type":"article","venue":"Future Internet","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Autoregressive model; Gaussian process; Covariance; Bayesian probability; Computer science; Kriging; Gaussian; Bayesian inference; Covariance function; Gaussian network model; Artificial intelligence; Data mining; Pattern recognition (psychology); Machine learning; Mathematics; Statistics","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":[0.00006183775,0.0001269355,0.0001143291,0.00009084349,0.00002631643,0.00005750547,0.00009276224,0.0001147675,0.00008340146],"category_scores_gemma":[0.000004672972,0.0001360352,0.00006634568,0.0001255169,0.00001145784,0.0001515207,0.00003220194,0.0002731003,0.000008611989],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007154002,"about_ca_system_score_gemma":0.00001501367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006500808,"about_ca_topic_score_gemma":0.00003409937,"domain_scores_codex":[0.9992867,0.0000223443,0.0002085679,0.000179963,0.0001371626,0.0001652711],"domain_scores_gemma":[0.9997293,0.00000441022,0.00001376036,0.0001593745,0.0000279567,0.00006523284],"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":[0.00002443291,0.00009145532,0.0001797151,0.0001656054,0.0001911968,0.0001151235,0.001087052,0.7943906,0.002294366,0.003279905,0.1245045,0.07367611],"study_design_scores_gemma":[0.0001420882,0.00001464803,0.00006860714,0.00004468436,0.00001747208,0.00002735476,0.0000865762,0.9753421,0.0007393666,0.00004262603,0.02336268,0.0001118078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1675489,0.0002236626,0.8227151,0.000196408,0.001860537,0.0000959504,0.00001565277,0.004544626,0.002799166],"genre_scores_gemma":[0.9892072,0.00006501346,0.009557459,0.00008246137,0.0008575809,0.000008236824,0.0000803289,0.0000306029,0.000111099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8216583,"threshold_uncertainty_score":0.5547354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01218702445997785,"score_gpt":0.2171545919510018,"score_spread":0.2049675674910239,"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."}}