{"id":"W2047308938","doi":"10.1080/07474930903039246","title":"Length-bias Correction in Transformation Models with Supplementary Data","year":2009,"lang":"en","type":"article","venue":"Econometric Reviews","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Estimator; Microdata (statistics); Robustness (evolution); Econometrics; Statistics; Mathematics; Observable; Truncation (statistics); Computer science; Census; Population; Demography","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":[0.03913983,0.0007374862,0.001686658,0.001753786,0.0008579003,0.0018789,0.003529154,0.002282814,0.006701961],"category_scores_gemma":[0.1842519,0.0009241421,0.001589248,0.002871733,0.002699516,0.004564539,0.003478242,0.002983386,0.001173779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001315347,"about_ca_system_score_gemma":0.00213604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003082146,"about_ca_topic_score_gemma":0.002757284,"domain_scores_codex":[0.9808835,0.01313858,0.0009257296,0.002121103,0.002290584,0.0006405346],"domain_scores_gemma":[0.856383,0.1135387,0.01144937,0.01436892,0.003764006,0.0004959552],"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":[0.000606861,0.0002148343,0.04344161,0.0006333006,0.0005272069,0.001180979,0.00122982,0.1264279,0.001811863,0.504388,0.005669331,0.3138683],"study_design_scores_gemma":[0.0001530433,0.0001621751,0.006554331,0.0001460316,0.0001337623,0.0003395541,0.0001431027,0.579232,0.002741737,0.4034579,0.006866093,0.00007036553],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0201706,0.0003054908,0.9773875,0.0005540567,0.00005395741,0.0001085351,0.0002705005,0.0002722198,0.0008770949],"genre_scores_gemma":[0.5810187,0.0008361104,0.4079815,0.0006746515,0.0003614726,0.0008940161,0.001738879,0.0002632986,0.006231378],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03913983,"threshold_uncertainty_score":0.2069936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3302682273843924,"score_gpt":0.2856564408571911,"score_spread":0.04461178652720132,"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."}}