{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01275782,0.0004928135,0.00149365,0.001690647,0.001066719,0.001325543,0.002791492,0.001175435,0.002228234],"category_scores_gemma":[0.03698939,0.000565453,0.001231838,0.003203066,0.0005678058,0.0009827277,0.001212334,0.002089843,0.0007410594],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000750516,"about_ca_system_score_gemma":0.001201436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005611034,"about_ca_topic_score_gemma":0.006250579,"domain_scores_codex":[0.9936064,0.003398584,0.0005338935,0.00114437,0.0006720945,0.0006446946],"domain_scores_gemma":[0.9573077,0.0190196,0.005540976,0.01322586,0.004305596,0.0006001981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002410842,0.0006703403,0.6397555,0.0005434779,0.001232018,0.001455497,0.00119367,0.1487068,0.002057072,0.006647499,0.01188785,0.1834394],"study_design_scores_gemma":[0.00008976687,0.0005781186,0.3689644,0.0002120455,0.0003694193,0.0007249183,0.0016977,0.5890095,0.008110279,0.01974106,0.01032621,0.0001766205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7090336,0.000458707,0.2782861,0.0005244302,0.0004530063,0.0001367693,0.008261543,0.0006942961,0.002151502],"genre_scores_gemma":[0.9467694,0.0001297601,0.04159144,0.00007935048,0.00009155388,0.0001257803,0.009529945,0.00007821383,0.001604617],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01275782,"threshold_uncertainty_score":0.06747061,"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."}}