{"id":"W303565404","doi":"10.1002/cjs.5550330201","title":"Inference for domains under imputation for missing survey data","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Survey Methodology and Nonresponse","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Statistics Canada","funders":"","keywords":"Imputation (statistics); Estimator; Missing data; Inference; Statistics; Econometrics; Efficiency; Best linear unbiased prediction; Mathematics; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02080891,0.00005545378,0.0001346776,0.0001138432,0.0004831233,0.0000710806,0.0003111253,0.0000776564,0.00005956611],"category_scores_gemma":[0.06065103,0.00005733782,0.00002098417,0.00009999563,0.0001896871,0.0001993659,0.000005574569,0.00008819768,0.000001678122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001422554,"about_ca_system_score_gemma":0.004792369,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.00980687,"about_ca_topic_score_gemma":0.6735218,"domain_scores_codex":[0.9975111,0.001780716,0.0002593536,0.00009001542,0.00009897603,0.0002598755],"domain_scores_gemma":[0.973574,0.02521844,0.0001991081,0.0001068212,0.0005427079,0.0003589364],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003368969,0.00006880723,0.05283693,0.00006348356,0.0003083631,0.0000566846,0.02433094,0.002193101,0.0001091041,0.1349974,0.2321016,0.5495645],"study_design_scores_gemma":[0.002069151,0.0003633662,0.5011188,0.00006705253,0.0001619255,0.00002272804,0.00365206,0.005197097,0.00005378377,0.1268377,0.3599885,0.0004678741],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01160935,0.0001396496,0.9825581,0.001452008,0.0005442483,0.0001313073,0.003481801,0.000002293357,0.00008119058],"genre_scores_gemma":[0.4654963,0.00002681432,0.533191,0.0004494778,0.0004275668,0.000001474784,0.0001830276,0.00001041529,0.0002139001],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6637149,"threshold_uncertainty_score":0.9967869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5591886833785065,"score_gpt":0.4977585733560223,"score_spread":0.06143011002248416,"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."}}