{"id":"W2116330103","doi":"10.1007/s13571-011-0009-9","title":"Improved prediction in finite population sampling using convex combination of parametric and non-parametric models","year":2010,"lang":"en","type":"article","venue":"Sankhya B","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Parametric statistics; Parametric model; Sample size determination; Sampling (signal processing); Population; Computer science; Mathematics; Inference; Mathematical optimization; Applied mathematics; Algorithm; Statistics; 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":[0.001047152,0.0001171063,0.0002453204,0.0008105624,0.00005907332,0.00003062427,0.00006770383,0.0001751457,0.0000105076],"category_scores_gemma":[0.001876886,0.0001224871,0.00004171722,0.0008931682,0.00003425871,0.0002747121,0.00002838244,0.0002272418,5.553383e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004073758,"about_ca_system_score_gemma":0.000021425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000631305,"about_ca_topic_score_gemma":0.00003713004,"domain_scores_codex":[0.9989653,0.0000511892,0.0004720353,0.0001956751,0.0001715943,0.000144199],"domain_scores_gemma":[0.9983959,0.0009487986,0.0002702785,0.0002082769,0.0001389721,0.0000377598],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003139429,0.001829091,0.7352142,0.001992892,0.0001651935,0.00000259315,0.003252944,0.0372787,0.06888474,0.04657404,0.0001172146,0.1043744],"study_design_scores_gemma":[0.0004072554,0.00004921203,0.05279465,0.0000592787,0.00002129887,0.000002356167,0.00002823953,0.8005154,0.002240008,0.1437789,0.000001182697,0.000102267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7306958,0.00002097381,0.2687338,0.000006178886,0.0001244518,0.0002592079,0.000011772,0.0000772769,0.00007052519],"genre_scores_gemma":[0.8804258,0.000009305279,0.119471,0.000005897945,0.00001837747,0.0000137197,0.00003229194,0.0000171007,0.000006491611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7632366,"threshold_uncertainty_score":0.4994878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1166487803878376,"score_gpt":0.3569477197248903,"score_spread":0.2402989393370526,"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."}}