{"id":"W1987764636","doi":"10.1109/icde.2014.6816748","title":"VoidWiz: Resolving incompleteness using network effects","year":2014,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Imputation (statistics); Computer science; Missing data; Knowledge graph; Analytics; Graph; Data mining; Value (mathematics); Data science; Machine learning; Theoretical computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.009270014,0.002301567,0.001471084,0.005305236,0.002057122,0.004809689,0.004409265,0.002073088,0.01404929],"category_scores_gemma":[0.06081516,0.001652286,0.001998421,0.003474687,0.001462177,0.01098803,0.007061214,0.003681229,0.002632376],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00140092,"about_ca_system_score_gemma":0.002636837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01220867,"about_ca_topic_score_gemma":0.01919771,"domain_scores_codex":[0.9963844,0.001153167,0.0002456095,0.0007960446,0.001226794,0.0001940087],"domain_scores_gemma":[0.9810377,0.01241431,0.00142751,0.00347609,0.001181475,0.0004629855],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001259815,0.0003699425,0.02252797,0.001333754,0.0007281079,0.001128568,0.002808195,0.2363764,0.006528371,0.181375,0.1242972,0.4212666],"study_design_scores_gemma":[0.000104123,0.00004697176,0.001153632,0.0001472122,0.00009448535,0.0001492489,0.0002460482,0.7721666,0.005367762,0.1858561,0.03459829,0.00006960803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01123093,0.0003925788,0.9394286,0.001713819,0.0002664671,0.0001841312,0.006493679,0.03748823,0.002801576],"genre_scores_gemma":[0.1868787,0.0007661051,0.7787787,0.000588921,0.000185658,0.0007272898,0.01856642,0.007430028,0.006078194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01404929,"threshold_uncertainty_score":0.04902512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02199000201513759,"score_gpt":0.2835746323535404,"score_spread":0.2615846303384028,"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."}}