{"id":"W3203478578","doi":"","title":"Estimation of annual average daily traffic based on partial and imputed permanent traffic count data.","year":2009,"lang":"en","type":"dissertation","venue":"oURspace (University of Regina)","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Faculty of Graduate Studies and Research, University of Regina; University of Regina","keywords":"Statistics; Estimation; Traffic count; Imputation (statistics); Count data; Computer science; Econometrics; Missing data; Geography; Transport engineering; Traffic volume; Mathematics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007376036,0.0004283603,0.0004821715,0.001320868,0.0001590638,0.0005079188,0.0007967736,0.0004115074,0.0008864542],"category_scores_gemma":[0.003726275,0.0003128204,0.0004513369,0.001414751,0.0001103954,0.0007100516,0.0003364243,0.0004658416,0.0004891006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003208316,"about_ca_system_score_gemma":0.0004301085,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01194752,"about_ca_topic_score_gemma":0.01208532,"domain_scores_codex":[0.9997224,0.00009016935,0.00001580988,0.00008338872,0.00005002448,0.00003809536],"domain_scores_gemma":[0.9984359,0.0006752595,0.000138732,0.0003355463,0.0003419171,0.00007250749],"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":[0.0005768409,0.0002754233,0.1718742,0.0001500518,0.0003648406,0.0001589189,0.00009384238,0.6634892,0.004091294,0.001608036,0.005105242,0.152212],"study_design_scores_gemma":[0.000006759131,0.00006004107,0.04560698,0.00001078705,0.00003404007,0.00005033354,0.00003759378,0.9514092,0.001363422,0.0007006144,0.0007066945,0.0000135202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9000295,0.0003885845,0.08760385,0.0001481573,0.00007027393,0.0000348843,0.008424798,0.001339674,0.001960239],"genre_scores_gemma":[0.9662333,0.0001353585,0.02364012,0.000008096414,0.00003146583,0.00002684561,0.009109049,0.0000413128,0.0007746498],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01194752,"threshold_uncertainty_score":0.02375597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006859286840852225,"score_gpt":0.2032610517882772,"score_spread":0.196401764947425,"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."}}