{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005965546,0.001111141,0.0014169,0.0008255816,0.0004809505,0.001242266,0.002267906,0.00168709,0.002143086],"category_scores_gemma":[0.01460861,0.0003890768,0.001389402,0.00129427,0.0003606364,0.001897163,0.001214619,0.001926372,0.0007224273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001018115,"about_ca_system_score_gemma":0.001572386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0147254,"about_ca_topic_score_gemma":0.007328548,"domain_scores_codex":[0.9985532,0.0005958985,0.0001196341,0.0002874649,0.0002684622,0.0001753817],"domain_scores_gemma":[0.9935947,0.003465126,0.0003775889,0.0006670274,0.0016405,0.0002550486],"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":[0.0008024065,0.0004212523,0.01350358,0.0001700543,0.0002354665,0.0002084163,0.00008073932,0.892114,0.001039883,0.002076451,0.002917371,0.08643031],"study_design_scores_gemma":[0.00001680236,0.0001011198,0.001097182,0.00001143301,0.00002728065,0.00001602816,0.0000176656,0.9970746,0.0006944019,0.0005883048,0.0003458121,0.000009400514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5899999,0.002778356,0.3857431,0.002144886,0.0006854657,0.0002959093,0.002847078,0.005699479,0.009805864],"genre_scores_gemma":[0.9430232,0.0004956572,0.05122949,0.0002686663,0.00009045219,0.0001064198,0.002567002,0.0001012378,0.002117792],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0147254,"threshold_uncertainty_score":0.03154922,"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."}}