{"id":"W2039417141","doi":"10.1016/j.trc.2004.07.006","title":"Estimation of missing traffic counts using factor, genetic, neural, and regression techniques","year":2004,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":177,"is_retracted":false,"has_abstract":false,"ca_institutions":"Saint Mary's University; University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Autoregressive integrated moving average; Missing data; Statistics; Regression; Percentile; Regression analysis; Autoregressive model; Artificial neural network; Computer science; Econometrics; Time series; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001211874,0.00009508416,0.0001115112,0.0003686702,0.00008390185,0.00001855919,0.00010785,0.0001033893,0.000006054317],"category_scores_gemma":[0.00002122697,0.0000909106,0.00002177351,0.0003025496,0.0001459844,0.0001335771,0.000008180483,0.0001844443,0.000001326993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003806813,"about_ca_system_score_gemma":0.00001210061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001190723,"about_ca_topic_score_gemma":0.000007010593,"domain_scores_codex":[0.9992397,0.00001182423,0.0002092825,0.0001345403,0.0002339995,0.0001705889],"domain_scores_gemma":[0.9997364,0.00001750446,0.00003176088,0.0001463574,0.00004734093,0.00002058468],"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":[0.000007964698,0.00003137337,0.0001798609,0.0003391419,0.00002511868,0.00001374221,0.0003409825,0.06278671,0.01240211,0.001397067,0.002429787,0.9200462],"study_design_scores_gemma":[0.0005805987,0.0001974845,0.005350127,0.001211348,0.00004384612,0.000007442349,0.001194798,0.6580147,0.3186261,0.003743888,0.01046359,0.0005660977],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8624083,0.001010225,0.1224991,0.0002772873,0.0000831843,0.0003789523,0.00001648232,0.01304961,0.0002768126],"genre_scores_gemma":[0.9720131,0.001236478,0.02669038,0.000002234766,0.000006549301,0.0000212771,0.000009598362,0.00001690633,0.000003455665],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.91948,"threshold_uncertainty_score":0.3707227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05432762398727279,"score_gpt":0.3431123694838062,"score_spread":0.2887847454965334,"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."}}