{"id":"W4390538970","doi":"10.1007/s00500-023-09523-9","title":"Structure identification of missing data: a perspective from granular computing","year":2024,"lang":"en","type":"article","venue":"Soft Computing","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Data mining; Cluster analysis; Granular computing; Granularity; Imputation (statistics); Missing data; Computer science; Fuzzy clustering; Data set; Fuzzy set; Fuzzy logic; Uncertain data; Partition (number theory); Algorithm; Rough set; Mathematics; Artificial intelligence; Machine learning","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":[0.01096313,0.001214093,0.004828395,0.007320841,0.001539571,0.01022881,0.004852045,0.003517891,0.001851434],"category_scores_gemma":[0.04109442,0.001357343,0.003010174,0.008544456,0.005212154,0.01522419,0.005638964,0.004464652,0.0002639168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002248048,"about_ca_system_score_gemma":0.002535935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003366985,"about_ca_topic_score_gemma":0.00186973,"domain_scores_codex":[0.9921038,0.003017475,0.0007497605,0.001124463,0.002457325,0.0005470013],"domain_scores_gemma":[0.9751142,0.01577442,0.002552617,0.003820609,0.002044714,0.0006935202],"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":[0.0002423938,0.0001421177,0.004354627,0.0008509193,0.0004811896,0.0006319471,0.0009910467,0.1108962,0.001692237,0.790629,0.002040335,0.08704802],"study_design_scores_gemma":[0.00001616315,0.00004969113,0.0006149628,0.0002108256,0.00008685798,0.0001572333,0.0004200541,0.2019876,0.0007534775,0.7933841,0.00226797,0.00005109581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01108448,0.002440626,0.9817795,0.002555191,0.000155045,0.00004856537,0.0001756989,0.00008560455,0.001675304],"genre_scores_gemma":[0.5475268,0.004909493,0.4440975,0.0006015301,0.0008252051,0.0001578379,0.000513947,0.00007845913,0.001289192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01096313,"threshold_uncertainty_score":0.05797923,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02721700621508678,"score_gpt":0.294996394086982,"score_spread":0.2677793878718952,"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."}}