{"id":"W2051299429","doi":"10.3141/1879-09","title":"Genetically Designed Models for Accurate Imputation of Missing Traffic Counts","year":2004,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"Saint Mary's University; University of Regina","funders":"National Research Council Canada","keywords":"Imputation (statistics); Percentile; Missing data; Statistics; Regression analysis; Computer science; Regression; Sample size determination; Artificial neural network; Data mining; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009773644,0.001270658,0.001849265,0.001425709,0.0006981166,0.001404298,0.003588965,0.002042074,0.002227621],"category_scores_gemma":[0.0306069,0.0009983263,0.00166295,0.00178372,0.001192599,0.0018829,0.00155514,0.003321134,0.0007228609],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001842881,"about_ca_system_score_gemma":0.001899412,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0103806,"about_ca_topic_score_gemma":0.008520559,"domain_scores_codex":[0.9954355,0.002661815,0.0002170406,0.0008939485,0.0005132501,0.0002784076],"domain_scores_gemma":[0.9843667,0.01100014,0.001739851,0.001008585,0.001703788,0.0001809469],"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":[0.00006790749,0.00004547326,0.002548573,0.00003966861,0.0001030204,0.00007007621,0.0001000592,0.9619643,0.000259928,0.01467301,0.0005189338,0.01960896],"study_design_scores_gemma":[0.00001219689,0.00003695503,0.0005071854,0.00001795972,0.00002385623,0.00002348987,0.00001129762,0.9860024,0.0002340017,0.01255079,0.0005624751,0.00001725097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01969022,0.0001615377,0.9785714,0.0001658793,0.00005122564,0.00006483956,0.0002419593,0.0003830976,0.0006699164],"genre_scores_gemma":[0.4478263,0.000596894,0.5425948,0.000362272,0.0001101699,0.0008964398,0.001969378,0.0001855861,0.005458213],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0103806,"threshold_uncertainty_score":0.05168855,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07824795864077157,"score_gpt":0.3560851005977445,"score_spread":0.2778371419569729,"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."}}