{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000124448,0.0002705324,0.0002610746,0.0001323275,0.00007078252,0.0004510469,0.000773215,0.0001958846,0.000004054488],"category_scores_gemma":[0.000009108555,0.0002735138,0.00004285007,0.0002088529,0.00001637557,0.0004801325,0.0003280471,0.0002698481,0.000001583293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001113771,"about_ca_system_score_gemma":0.00004648141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001649294,"about_ca_topic_score_gemma":0.0002626982,"domain_scores_codex":[0.9987376,0.00002631917,0.000262533,0.0006159332,0.0001591705,0.0001984485],"domain_scores_gemma":[0.9989548,0.00003759343,0.00008333463,0.0007134357,0.0001377515,0.0000730657],"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":[0.00003270907,0.0000211318,0.000002666095,0.0001017237,0.0001600377,0.000001833471,0.0003810085,0.9212259,0.00009165583,0.00005339941,0.03159405,0.04633388],"study_design_scores_gemma":[0.000323556,0.00003691139,0.00007319177,0.00009148849,0.0001047209,9.85303e-7,0.00006936851,0.9946201,0.001176284,0.0000252309,0.003165007,0.0003131147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00799389,0.00009132816,0.9854258,0.0001143853,0.001335206,0.002152551,0.000226961,0.002456922,0.0002029886],"genre_scores_gemma":[0.9724594,0.0000947141,0.0208935,0.0001987826,0.0008652497,0.001374328,0.004035287,0.00006349829,0.00001519184],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9645323,"threshold_uncertainty_score":0.9999717,"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."}}