{"id":"W4392185333","doi":"10.1525/collabra.92993","title":"Multiple Imputation When Variables Exceed Observations: An Overview of Challenges and Solutions","year":2024,"lang":"en","type":"article","venue":"Collabra Psychology","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Imputation (statistics); Computer science; Data science; Missing data; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002493309,0.0001030938,0.0002299816,0.00007565954,0.00005619793,0.00001859414,0.00008957543,0.000102432,0.0001331533],"category_scores_gemma":[0.0004441265,0.0000910386,0.00003022607,0.0001884948,0.0001142261,0.0001243226,0.00003822017,0.00008491141,0.00001213389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001271722,"about_ca_system_score_gemma":0.00002442861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001812615,"about_ca_topic_score_gemma":0.00003715513,"domain_scores_codex":[0.9990531,0.0001075141,0.0002868448,0.000279519,0.0001001773,0.0001728713],"domain_scores_gemma":[0.9984744,0.0011226,0.00004987925,0.0001954086,0.00008673281,0.0000709411],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002058062,0.0002204784,0.00005653933,0.0003885537,0.00006245539,0.000006038799,0.001272581,0.000001243233,0.0006550315,0.8299358,0.004429345,0.1629514],"study_design_scores_gemma":[0.0003306171,0.0002211638,0.01272262,0.00008021627,0.00005189246,0.00001920306,0.0001261346,0.01092692,0.00001565501,0.9662744,0.009116578,0.0001146038],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1126753,0.2173874,0.6193807,0.03357023,0.003288287,0.002046779,0.0007416161,0.001171593,0.009738187],"genre_scores_gemma":[0.3123762,0.01779766,0.6669808,0.0009429025,0.0004454023,0.000278609,0.0001048981,0.0001005455,0.0009729518],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.199701,"threshold_uncertainty_score":0.3712446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3706007486555401,"score_gpt":0.4286378990817513,"score_spread":0.05803715042621116,"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."}}