{"id":"W2790828348","doi":"","title":"Mixture Model Averaging for Clustering and Classification","year":2012,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Cluster analysis; Mixture model; Computer science; Bayesian information criterion; Model selection; Data mining; Rand index; Artificial intelligence; Bayesian inference; Bayesian probability; Pattern recognition (psychology); Mathematics; Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0002568624,0.0000943015,0.00009304725,0.00006852702,0.0001358424,0.0000415544,0.0002605064,0.00006523677,0.000001124891],"category_scores_gemma":[0.00001132756,0.00009940095,0.00004254096,0.000160063,0.00002417715,0.000740575,0.0001280261,0.000074609,0.000003398592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003461984,"about_ca_system_score_gemma":0.0000170513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002291554,"about_ca_topic_score_gemma":0.00000183442,"domain_scores_codex":[0.9993385,0.00003675537,0.0000664752,0.0002902087,0.00002673393,0.0002413277],"domain_scores_gemma":[0.9994547,0.00005401987,0.00004857929,0.0002833055,0.00003859705,0.0001207833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009969011,0.00002423539,0.0007094599,0.00002220319,0.00001064285,0.00000169939,0.0005613312,0.00475369,0.001215965,0.9793377,0.0001665987,0.01318649],"study_design_scores_gemma":[0.0002219068,0.000009809183,0.0004693929,0.000007181456,0.00001217412,0.000003998441,0.00001384582,0.9520972,0.0001464779,0.04648707,0.0004077408,0.0001232293],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02041144,0.00005866108,0.9776061,0.0001221916,0.0001182896,0.0001217052,0.000001553547,0.00007365498,0.001486432],"genre_scores_gemma":[0.7614637,0.00002043972,0.2377312,0.0001281045,0.00003803691,6.002136e-7,8.378581e-7,0.000005492432,0.0006116101],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9473435,"threshold_uncertainty_score":0.4053453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1081523921093083,"score_gpt":0.2151832802872695,"score_spread":0.1070308881779612,"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."}}