{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003892754,0.0005621538,0.0005978733,0.000393776,0.0002118329,0.0007199257,0.002491086,0.000479521,0.00001554603],"category_scores_gemma":[0.00003861473,0.000481387,0.0005460288,0.0003532215,0.00006175825,0.0002064213,0.002123252,0.01290606,0.0001182002],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001549646,"about_ca_system_score_gemma":0.008685273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007121661,"about_ca_topic_score_gemma":0.0002217176,"domain_scores_codex":[0.9939782,0.0004551015,0.00059361,0.000996087,0.0006002771,0.003376721],"domain_scores_gemma":[0.9981531,0.00006421235,0.0003472389,0.001005871,0.0001589653,0.0002706215],"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":[0.0000156954,0.00003082995,0.000002928103,0.00002348444,0.0002692035,0.00007912196,0.0002707308,0.00008489615,0.00001607634,0.8136305,0.0007757284,0.1848008],"study_design_scores_gemma":[0.0002907419,0.0001846837,0.000004553436,0.0002033743,0.00008761869,0.001281876,0.00005123455,0.03797033,0.00005836857,0.9580976,0.001270747,0.0004988327],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003107946,0.0215623,0.9655252,0.002738449,0.002812618,0.0002646172,0.000006639611,0.0002319202,0.006547506],"genre_scores_gemma":[0.5644217,0.009233443,0.4139115,0.0005648588,0.002468835,0.00004539868,0.000006936878,0.0001423756,0.009204947],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5641109,"threshold_uncertainty_score":0.9997638,"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."}}