{"id":"W2526660935","doi":"10.1515/jbnst-2004-1-217","title":"German Register Data for Regression Estimation in Survey Sampling – A Study on the German Microcensus Respecting for Data Protection / Stichproben-Regressionsschätzungen im deutschen Mikrozensus mit Registerdaten unter Berücksichtigung des Datenschutzes","year":2004,"lang":"en","type":"article","venue":"Jahrbücher für Nationalökonomie und Statistik","topic":"Survey Methodology and Nonresponse","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"German; Estimator; Estimation; Matching (statistics); Econometrics; Statistics; Sampling (signal processing); Sample (material); Population; Identification (biology); Computer science; Mathematics; Demography; Geography; Economics; Sociology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.04763816,0.0002237538,0.0006094725,0.002774078,0.0008858872,0.001755,0.001153412,0.0006861021,0.004961506],"category_scores_gemma":[0.1209472,0.000315258,0.0005130033,0.007934273,0.001407903,0.001334614,0.001131445,0.0005827706,0.0005728006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002765604,"about_ca_system_score_gemma":0.002955321,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03447825,"about_ca_topic_score_gemma":0.02681829,"domain_scores_codex":[0.8888814,0.09686034,0.003285739,0.002492557,0.007356908,0.001123045],"domain_scores_gemma":[0.8319147,0.135868,0.01175344,0.01177937,0.008134559,0.0005499985],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009305993,0.0002971296,0.5044785,0.00115291,0.0006468671,0.0006375514,0.006830057,0.01219932,0.0006531375,0.1920902,0.01534158,0.2647422],"study_design_scores_gemma":[0.0003819936,0.001328239,0.8208348,0.001512077,0.0008323995,0.0006871528,0.01311472,0.03889229,0.002604443,0.02598453,0.09368486,0.0001425951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8937572,0.009820214,0.05479183,0.006498204,0.0001877291,0.001242655,0.002913593,0.00009588467,0.03069281],"genre_scores_gemma":[0.9838283,0.00125688,0.01094096,0.0003081692,0.00005593838,0.0004588581,0.001177937,0.00002227606,0.001950671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9523618,"threshold_uncertainty_score":0.2519377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6249061368714397,"score_gpt":0.5663620139468306,"score_spread":0.05854412292460909,"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."}}