{"id":"W4205322208","doi":"10.1109/smc52423.2021.9659085","title":"Evaluation of Imputation Models Based on the Enhancement to Yield Forecasting","year":2021,"lang":"en","type":"article","venue":"2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC)","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Mitacs; Electronics and Telecommunications Research Institute","keywords":"Imputation (statistics); Missing data; Computer science; Ensemble forecasting; Artificial intelligence; Deep learning; Data modeling; Convolutional neural network; Artificial neural network; Machine learning; Residual; Data mining; Algorithm","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":[0.003676026,0.0001514674,0.0002061604,0.0001740612,0.0001221129,0.0003503144,0.0004538907,0.00006958535,0.0005893108],"category_scores_gemma":[0.001677713,0.0001107364,0.00006747637,0.0003232949,0.00005047947,0.00008514914,0.00007488109,0.0001252744,0.00005167313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009212727,"about_ca_system_score_gemma":0.000220507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001046732,"about_ca_topic_score_gemma":0.00008371731,"domain_scores_codex":[0.9959128,0.0002614179,0.0006784691,0.0004721167,0.002524878,0.000150357],"domain_scores_gemma":[0.9948725,0.001073499,0.0003715036,0.0004601219,0.003152832,0.00006951635],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008418513,0.0003313465,0.000200706,0.00002532583,0.00009303296,0.000005674722,0.001014151,0.2274868,0.007745685,0.6374965,0.01282816,0.1126885],"study_design_scores_gemma":[0.0001396264,0.0001246815,0.0001122353,0.0003271861,0.00002532773,0.000004001531,0.0006654878,0.9576614,0.006433162,0.0331944,0.001194101,0.000118348],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4236262,0.00009548067,0.35566,0.008939245,0.001354726,0.0014937,0.0001704574,0.00004210151,0.2086181],"genre_scores_gemma":[0.9965857,0.00001888673,0.001053115,0.000346681,0.0001111336,0.0001870124,0.000017199,0.00001020121,0.001670101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7301747,"threshold_uncertainty_score":0.645254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4714604009143857,"score_gpt":0.4233501463522049,"score_spread":0.04811025456218082,"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."}}