{"id":"W4229069577","doi":"10.1155/2022/4840021","title":"Data Imputation for Detected Traffic Volume of Freeway Using Regression of Multilayer Perceptron","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Imputation (statistics); Computer science; Missing data; Multivariate statistics; Data mining; Traffic volume; Multilayer perceptron; Statistics; Artificial intelligence; Artificial neural network; Machine learning; Mathematics; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002069167,0.00008240608,0.0001846968,0.0002119293,0.00004044163,0.00000271646,0.0001642098,0.00002916039,0.00001462592],"category_scores_gemma":[0.0000121739,0.00008092441,0.00007687646,0.000156973,0.00001350565,0.0003859657,0.000004536028,0.0001140917,3.332754e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005324304,"about_ca_system_score_gemma":0.00001954469,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001868172,"about_ca_topic_score_gemma":0.00001288368,"domain_scores_codex":[0.9990262,0.00001769194,0.0005591507,0.00008419281,0.0002327745,0.00007997281],"domain_scores_gemma":[0.9993416,0.00002339065,0.0003624317,0.0001304181,0.0001174936,0.00002467722],"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":[0.0001219873,0.00004179974,0.00005838167,0.0001188938,0.00003313014,0.000001594157,0.0006377443,0.8446131,0.1165567,0.000009502094,0.0002803085,0.03752689],"study_design_scores_gemma":[0.002403573,0.0006245119,0.02942145,0.0001750932,0.0002532534,0.00000900285,0.0022342,0.9421854,0.01966652,0.00004964593,0.002793836,0.0001835389],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6621393,0.0001458316,0.3370125,0.000008999816,0.0002560739,0.0001743107,0.0001594498,0.0001007798,0.000002765297],"genre_scores_gemma":[0.9420332,0.00007107824,0.05768919,0.000002695107,0.00002529055,0.000005818347,0.0001503912,0.00001893826,0.000003351409],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.279894,"threshold_uncertainty_score":0.3300002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02126281898102145,"score_gpt":0.2762991983234441,"score_spread":0.2550363793424227,"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."}}