{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009192943,0.001316282,0.004540539,0.003274833,0.001736243,0.004320368,0.005398301,0.004514044,0.007620507],"category_scores_gemma":[0.02993487,0.002002956,0.003205983,0.004248965,0.00221828,0.00597491,0.004411479,0.004924291,0.003790748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002071087,"about_ca_system_score_gemma":0.001984501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005110487,"about_ca_topic_score_gemma":0.009766296,"domain_scores_codex":[0.991567,0.004645544,0.0003989275,0.001652435,0.001114828,0.0006213295],"domain_scores_gemma":[0.9858459,0.009940526,0.0005408856,0.002075474,0.001236854,0.0003605262],"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":[0.001048027,0.0003684228,0.003675714,0.0006117558,0.0004274704,0.0002281352,0.0008047346,0.2402008,0.003408064,0.4033467,0.01582972,0.3300505],"study_design_scores_gemma":[0.00004144419,0.00002973328,0.0003973207,0.00005659312,0.00005935661,0.00008037538,0.00004441913,0.8171443,0.00057735,0.1790261,0.002504413,0.00003858119],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008558362,0.0007340587,0.9881407,0.0003281545,0.00009170043,0.00006732995,0.000253748,0.0003760879,0.001449817],"genre_scores_gemma":[0.385079,0.00202871,0.5871656,0.001111919,0.0008280854,0.001025144,0.00421461,0.0008022807,0.0177446],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009192943,"threshold_uncertainty_score":0.04861754,"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."}}