{"id":"W2739457506","doi":"10.1002/cjs.11326","title":"Testing perfect rankings in ranked‐set sampling with binary data","year":2017,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Imperfect; Type I and type II errors; Statistics; Statistic; Test statistic; Mathematics; Null hypothesis; Statistical hypothesis testing; Perfect information; Binary number; Test (biology); Set (abstract data type); Econometrics; Null (SQL); Sample size determination; Sampling (signal processing); Computer science; Mathematical economics; Data mining; Arithmetic","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08186495,0.0008011719,0.001982067,0.002843555,0.002073406,0.003292084,0.002995494,0.001631554,0.006857218],"category_scores_gemma":[0.4215729,0.0006434786,0.001585781,0.004235469,0.005798072,0.003960045,0.002877764,0.002879406,0.0008599964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001968348,"about_ca_system_score_gemma":0.003276279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005599815,"about_ca_topic_score_gemma":0.003678381,"domain_scores_codex":[0.8726208,0.0949657,0.004613678,0.008772165,0.01675177,0.00227589],"domain_scores_gemma":[0.4466069,0.487628,0.0225201,0.02780656,0.01284787,0.002590616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.004658487,0.001077827,0.35097,0.001704565,0.00313415,0.0007907952,0.002726848,0.06321247,0.001914949,0.3349363,0.01407432,0.2207993],"study_design_scores_gemma":[0.0005919121,0.001578955,0.08604494,0.0006122747,0.0005248113,0.0005213016,0.001608864,0.3274222,0.006165448,0.5668552,0.007817503,0.0002566601],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4427943,0.0005997072,0.5426993,0.001432144,0.0002626313,0.0005897316,0.001788603,0.0004794582,0.009354172],"genre_scores_gemma":[0.936562,0.00006806728,0.06130091,0.0002680416,0.00008361519,0.000311066,0.0008963742,0.00005241235,0.0004575126],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08186495,"threshold_uncertainty_score":0.4329484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3875572739143564,"score_gpt":0.3952332945535968,"score_spread":0.007676020639240444,"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."}}