{"id":"W2146876168","doi":"10.1111/j.0006-341x.2002.00964.x","title":"Ranked Set Sampling: Cost and Optimal Set Size","year":2002,"lang":"en","type":"article","venue":"Biometrics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wycliffe College","funders":"","keywords":"RSS; Ranking (information retrieval); Simple random sample; Sampling (signal processing); Set (abstract data type); Statistics; Computer science; Sample size determination; Population; Data mining; Mathematics; Information retrieval; Medicine; Telecommunications","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001711395,0.0001056731,0.000146623,0.0001996454,0.0001326433,0.00006927977,0.00009582112,0.00007536417,0.00112969],"category_scores_gemma":[0.004271499,0.0001006891,0.00003168169,0.001793922,0.000086999,0.00004775273,0.00003875511,0.00007816037,0.0002166611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004126504,"about_ca_system_score_gemma":0.000006894836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000252288,"about_ca_topic_score_gemma":2.946921e-7,"domain_scores_codex":[0.9991545,0.00002163634,0.0002444869,0.0001831937,0.0002110645,0.0001850851],"domain_scores_gemma":[0.9976526,0.001814025,0.00007835097,0.0001934669,0.0001056487,0.0001558341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000009830225,0.0003423181,0.0004064493,0.0001114624,0.00005317269,0.000004147514,0.0002774935,0.000006636662,0.0004167049,0.8377622,0.1331568,0.02745277],"study_design_scores_gemma":[0.006287767,0.0002895139,0.05167862,0.00007352467,0.0003868899,0.0001285626,0.0006997033,0.1806597,0.001170749,0.1282191,0.6285756,0.001830246],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02834566,0.0001503432,0.9655128,0.001060643,0.00007729069,0.0004127409,0.001658671,0.0001842798,0.00259754],"genre_scores_gemma":[0.8807451,0.0000725249,0.1180381,0.0002088012,0.0000459409,0.00005113665,0.0001136927,0.00001730357,0.0007073485],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8523995,"threshold_uncertainty_score":0.9997834,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2995083954626483,"score_gpt":0.410846597518567,"score_spread":0.1113382020559187,"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."}}