{"id":"W3176357659","doi":"10.1109/access.2021.3103456","title":"IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data","year":2021,"lang":"en","type":"preprint","venue":"IEEE Access","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Division of Civil, Mechanical and Manufacturing Innovation; U.S. Department of Transportation; National Science Foundation","keywords":"Imputation (statistics); Computer science; Generative grammar; Missing data; Data mining; Artificial neural network; Artificial intelligence; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003749919,0.00144208,0.001247491,0.0007775871,0.0005731985,0.0008762797,0.002456768,0.001414815,0.002950922],"category_scores_gemma":[0.009276056,0.0008580523,0.001331059,0.001197172,0.001100907,0.001240714,0.002533495,0.004008934,0.0009412232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009620027,"about_ca_system_score_gemma":0.001358618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006067438,"about_ca_topic_score_gemma":0.006453618,"domain_scores_codex":[0.9985991,0.0007675763,0.00005320195,0.0002554502,0.0002261237,0.00009841376],"domain_scores_gemma":[0.9964653,0.002535434,0.0001969382,0.0003240834,0.0003776152,0.0001005758],"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":[0.00006271084,0.00004067671,0.0009215834,0.00006269122,0.00009901051,0.00007241304,0.00007821224,0.9198762,0.0005617802,0.01370142,0.00347568,0.06104762],"study_design_scores_gemma":[0.000003281326,0.000006351948,0.00005010967,0.000004982739,0.000003311739,0.00001034505,0.000003315291,0.9933326,0.0002245038,0.005933569,0.0004241695,0.00000352888],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001486973,0.0001434966,0.9968712,0.0001527024,0.00003264927,0.00002784654,0.00007880975,0.0007170328,0.0004893406],"genre_scores_gemma":[0.2799349,0.0007489889,0.7099836,0.0006741835,0.0002209443,0.0006007749,0.001570602,0.0006572074,0.005608943],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006067438,"threshold_uncertainty_score":0.01983172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02787895434046487,"score_gpt":0.3007015653419617,"score_spread":0.2728226110014969,"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."}}