{"id":"W2018536683","doi":"10.1111/j.1467-842x.2010.00584.x","title":"SAMPLING AND ESTIMATION IN THE PRESENCE OF CUT‐OFF SAMPLING","year":2010,"lang":"en","type":"article","venue":"Australian & New Zealand Journal of Statistics","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Mathematics; Sampling (signal processing); Statistics; Estimator; Sampling bias; Sampling design; Sample (material); Selection (genetic algorithm); Sample size determination; Calibration; Set (abstract data type); Population; Selection bias; Probability sampling; Cluster sampling; Computer science; 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":[0.0006940642,0.00006677157,0.0001867986,0.0000873875,0.00003022845,0.00003786019,0.0001167157,0.00004703279,0.0001066661],"category_scores_gemma":[0.0001570538,0.00005959802,0.0000238381,0.00005013744,0.00005295669,0.0001751853,0.00001184032,0.0002136494,0.000008476658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001772897,"about_ca_system_score_gemma":0.00001641863,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000274189,"about_ca_topic_score_gemma":0.0001255756,"domain_scores_codex":[0.999161,0.0000106823,0.0005977767,0.00008461487,0.00003968787,0.0001062496],"domain_scores_gemma":[0.9991229,0.000185757,0.0005395614,0.00009047339,0.00001181884,0.00004945521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002368549,0.00007010799,0.9372408,0.00002755581,0.00002839764,0.000003655251,0.002164447,0.004492654,0.0003040391,0.03719284,0.002917213,0.01553455],"study_design_scores_gemma":[0.000549979,0.0001090286,0.9208812,0.00002714982,0.00001215104,0.00004245222,0.0001751294,0.001798089,0.00004601364,0.0719849,0.004290716,0.00008324116],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9790782,0.00007952683,0.0196645,0.0007370157,0.0002000773,0.00007160143,0.0000833595,0.000001064655,0.00008462246],"genre_scores_gemma":[0.952179,0.0002500687,0.04718611,0.00003188352,0.00006502267,5.688901e-7,0.00000794135,0.000005915578,0.0002734573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03479206,"threshold_uncertainty_score":0.2430337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1095235073761838,"score_gpt":0.2850730236934445,"score_spread":0.1755495163172607,"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."}}