{"id":"W4385974194","doi":"10.2139/ssrn.4545321","title":"Hierarchical Mixture of Discriminative Generalized Dirichlet Classifiers","year":2023,"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":"Université de Sherbrooke","funders":"","keywords":"Discriminative model; Latent Dirichlet allocation; Mathematics; Pattern recognition (psychology); Artificial intelligence; Hierarchical Dirichlet process; Dirichlet distribution; Computer science; Topic model; Mathematical analysis","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":[0.003815355,0.0004842945,0.0008277813,0.0004559246,0.0001899486,0.0001906785,0.002697703,0.0005276527,0.00000669959],"category_scores_gemma":[0.0001883179,0.000389384,0.0006160908,0.0004574681,0.0001643492,0.0002092414,0.001364437,0.008343305,0.000009209128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007402104,"about_ca_system_score_gemma":0.004939712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006299353,"about_ca_topic_score_gemma":0.0001079303,"domain_scores_codex":[0.9941495,0.0008980629,0.0007591224,0.0008051848,0.0007524111,0.002635725],"domain_scores_gemma":[0.9977216,0.0001749244,0.0007063939,0.0009205077,0.0002490044,0.0002276447],"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":[0.00003052019,0.00006526648,0.00002683158,0.00004922224,0.0003800762,0.00002538811,0.0009134859,0.00008812705,0.0004678604,0.93454,0.0007336886,0.06267959],"study_design_scores_gemma":[0.0004799218,0.000188527,0.0001613193,0.0001280707,0.00007925121,0.0001749396,0.00008745183,0.009175,0.0002665756,0.98834,0.0005134306,0.0004055659],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002751364,0.002866209,0.9865193,0.005131396,0.00156952,0.0002653309,0.00001793571,0.000126123,0.0007527979],"genre_scores_gemma":[0.2329686,0.02135322,0.732224,0.0005264835,0.001737114,0.00007900342,0.00006886493,0.0001904447,0.01085224],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2542953,"threshold_uncertainty_score":0.9998558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03167931280560105,"score_gpt":0.3034732745624512,"score_spread":0.2717939617568502,"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."}}