{"id":"W1990449216","doi":"10.2307/3315913","title":"A general class of hierarchical ordinal regression models with applications to correlated roc analysis","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Markov chain Monte Carlo; Ordinal regression; Statistics; Bayesian probability; Artificial intelligence; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"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":[0.01647039,0.001917424,0.002232506,0.003259804,0.000936807,0.002894022,0.003885159,0.002753194,0.006078121],"category_scores_gemma":[0.04592633,0.001398167,0.00391484,0.004399411,0.0018062,0.003135067,0.002864503,0.003973139,0.002102467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001832224,"about_ca_system_score_gemma":0.00267513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005957094,"about_ca_topic_score_gemma":0.005920104,"domain_scores_codex":[0.9884827,0.006792468,0.0006594243,0.001347956,0.002188467,0.0005289803],"domain_scores_gemma":[0.9712516,0.020633,0.002413807,0.002944645,0.002303944,0.0004528995],"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":[0.00006533965,0.0001320699,0.003213509,0.0003159126,0.000288115,0.0003076509,0.0004346666,0.127017,0.0008985226,0.7257875,0.01037864,0.1311612],"study_design_scores_gemma":[0.00003063682,0.00006286279,0.001115794,0.0001016617,0.0000838837,0.0002468107,0.00004404366,0.5129381,0.000282765,0.4717289,0.0133001,0.00006432212],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001283249,0.0004509032,0.9960873,0.0004881782,0.00005297651,0.0000808337,0.0001561289,0.0002424506,0.001157987],"genre_scores_gemma":[0.1307345,0.002618003,0.854474,0.0009569466,0.0006788626,0.001340142,0.0008793763,0.0003709666,0.007947163],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01647039,"threshold_uncertainty_score":0.0871048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679690788041081,"score_gpt":0.2588644484062906,"score_spread":0.2420675405258798,"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."}}