{"id":"W4400767755","doi":"10.2139/ssrn.4899072","title":"Bayesian Adaptive Sparse Copula","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Copula (linguistics); Bayesian probability; Econometrics; Computer science; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002077233,0.0009840605,0.001681431,0.001245222,0.000638368,0.001834924,0.001946628,0.002340177,0.008445962],"category_scores_gemma":[0.0154702,0.001237745,0.001310045,0.001737981,0.001445089,0.002975593,0.002461459,0.002439696,0.002003344],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009581736,"about_ca_system_score_gemma":0.000931107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00241951,"about_ca_topic_score_gemma":0.002898032,"domain_scores_codex":[0.998778,0.0004691605,0.00004434417,0.0003079032,0.0002994707,0.0001011131],"domain_scores_gemma":[0.9957105,0.002520636,0.000353337,0.0007042678,0.0005391467,0.0001720705],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001469742,0.00008192867,0.001412888,0.000121586,0.0001201335,0.0001939208,0.000140593,0.5881466,0.003092208,0.3228162,0.008744257,0.07498267],"study_design_scores_gemma":[0.000006264846,0.000006256702,0.000208108,0.000005970255,0.00000676058,0.00004108526,0.00000642494,0.9484022,0.0002069591,0.05028747,0.000811686,0.00001078519],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008516298,0.0001748452,0.9874856,0.0002857324,0.00005806545,0.00002340492,0.0001326303,0.0002218287,0.003101519],"genre_scores_gemma":[0.5511301,0.001018845,0.4134604,0.00068729,0.0005450825,0.000359227,0.001434556,0.0007334411,0.03063105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008445962,"threshold_uncertainty_score":0.02825451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01751315336768254,"score_gpt":0.2739250269688807,"score_spread":0.2564118736011982,"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."}}