{"id":"W2016903344","doi":"10.1080/03081060802364505","title":"Imputation of Missing Traffic Data during Holiday Periods","year":2008,"lang":"en","type":"article","venue":"Transportation Planning and Technology","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada; University of Regina","keywords":"Adaptability; Imputation (statistics); Transport engineering; Computer science; Parametric statistics; Data collection; Regression; Missing data; Data mining; Engineering; Statistics; Mathematics; Machine learning","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":[0.000047338,0.00008293167,0.0001223412,0.0002877495,0.00007223685,0.000004491037,0.0001072856,0.00009470351,0.000002922236],"category_scores_gemma":[0.000004648254,0.00009253828,0.00001047606,0.0002213728,0.00007754061,0.0001382127,0.000004743806,0.0001020138,6.288405e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006363729,"about_ca_system_score_gemma":0.000006406438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002200036,"about_ca_topic_score_gemma":0.000002877649,"domain_scores_codex":[0.9994841,0.000003844682,0.0001981439,0.0001421834,0.0000659003,0.000105822],"domain_scores_gemma":[0.9997524,0.00000830259,0.00003486796,0.0001658511,0.00001680166,0.00002176211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001589516,0.0003007633,0.1204215,0.002725849,0.0006868367,0.001207171,0.02172664,0.2065352,0.1206872,0.006352538,0.01046991,0.5087275],"study_design_scores_gemma":[0.002765703,0.0002162732,0.578272,0.0004431799,0.0001859301,0.0004445893,0.002170875,0.3619033,0.0466307,0.0003686353,0.005647065,0.0009516952],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9504061,0.0004739225,0.04520079,0.00007601432,0.0000526362,0.00007476576,0.00002308468,0.003521204,0.0001715108],"genre_scores_gemma":[0.9959961,0.0001522076,0.003679875,0.000003996398,0.000008694804,0.000005781127,0.0001319133,0.00001321079,0.000008292635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5077758,"threshold_uncertainty_score":0.3773602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01793496878795373,"score_gpt":0.2359108532562329,"score_spread":0.2179758844682792,"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."}}