{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003398689,0.00007843805,0.0001098697,0.00004554671,0.0001505765,0.0002191314,0.0005249545,0.00002714085,0.00001248088],"category_scores_gemma":[0.0000836404,0.00006788384,0.00002822895,0.0004056373,0.0000165022,0.0003101258,0.000312759,0.00004410877,0.00005091293],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001442809,"about_ca_system_score_gemma":0.00001822414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002511389,"about_ca_topic_score_gemma":0.000009084563,"domain_scores_codex":[0.9991933,0.00009412144,0.0001329642,0.0002098645,0.0001566285,0.0002131686],"domain_scores_gemma":[0.9992899,0.0001410714,0.00004781395,0.0004052432,0.00004417983,0.00007184492],"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":[8.51833e-7,0.00002527862,0.00303088,0.0000351436,0.00001070814,0.000004183884,0.0000724782,0.009260708,0.0005319043,0.9621467,0.006008255,0.01887292],"study_design_scores_gemma":[0.0001152409,0.00001559495,0.0007903781,0.00002766931,0.000003114337,0.000003157543,0.000001832474,0.9783201,0.0002698333,0.003726626,0.01661794,0.0001084687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002006112,0.0000170863,0.9930583,0.0001005033,0.0003214967,0.00004122758,1.724466e-7,0.0001679612,0.004287196],"genre_scores_gemma":[0.7238516,0.000003593179,0.2717335,0.003532836,0.00036939,0.000001444466,0.000005995952,0.00001245522,0.0004892494],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9690595,"threshold_uncertainty_score":0.2768223,"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."}}