{"id":"W4399827830","doi":"10.1002/sim.10144","title":"Bayesian mixture modelling with ranked set samples","year":2024,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gibbs sampling; Bayesian probability; Ranking (information retrieval); Bayesian average; Computer science; Statistics; Sampling (signal processing); RSS; Simple random sample; Bayesian inference; Data mining; Variable-order Bayesian network; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007913297,0.0001974599,0.0003105909,0.0002203855,0.00005559964,0.00007741178,0.0004180847,0.00007335548,0.00004959834],"category_scores_gemma":[0.00004759594,0.0001318733,0.00001755562,0.0005854199,0.000126704,0.0001431081,0.00005471412,0.0003818257,0.000006559386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004082745,"about_ca_system_score_gemma":0.0001059205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001121935,"about_ca_topic_score_gemma":0.00005877302,"domain_scores_codex":[0.9984085,0.0001180069,0.0002989683,0.0004775601,0.0003787057,0.0003182392],"domain_scores_gemma":[0.9988124,0.0005451204,0.00003846761,0.000419962,0.00006272997,0.000121339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000113423,0.0000103198,0.00003525774,0.0001361945,0.00002904287,0.0004777289,0.003288145,0.0009595926,0.00005042876,0.9169269,0.00733848,0.07073662],"study_design_scores_gemma":[0.0003122135,0.00011324,0.00002407523,0.000374145,0.00001894004,0.00004291437,0.00003466525,0.6089669,0.0000231993,0.3825817,0.007359631,0.0001482854],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003952904,0.001631927,0.993467,0.002082817,0.000497418,0.0001621029,0.00004682761,0.0001293424,0.001943073],"genre_scores_gemma":[0.05611474,0.0001698646,0.9427068,0.0004649976,0.0002051376,0.00001249737,0.00002707559,0.00002160637,0.0002773352],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6080074,"threshold_uncertainty_score":0.5377639,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03274320391658409,"score_gpt":0.3164800722356023,"score_spread":0.2837368683190182,"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."}}