{"id":"W4313564081","doi":"10.1109/icit48603.2022.10002736","title":"Fully Bayesian Libby-Novick Beta Mixture Model with Feature Selection","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Industrial Technology (ICIT)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"BETA (programming language); Feature selection; Bayesian probability; Artificial intelligence; Computer science; Feature (linguistics); Pattern recognition (psychology); Model selection; Selection (genetic algorithm); Philosophy","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":[0.002544898,0.001383967,0.001939123,0.00164882,0.001003055,0.001753108,0.004222231,0.001759436,0.003003019],"category_scores_gemma":[0.005704192,0.0009900366,0.001637374,0.001686676,0.0008561751,0.002194734,0.002409905,0.002342599,0.002026387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001133105,"about_ca_system_score_gemma":0.001660636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00768509,"about_ca_topic_score_gemma":0.006818799,"domain_scores_codex":[0.9981475,0.0006217211,0.00008615078,0.0004220239,0.0005451604,0.0001774247],"domain_scores_gemma":[0.9986652,0.0005798977,0.0001266927,0.0001779759,0.0003752471,0.00007495779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004577496,0.0002071862,0.004867079,0.0001974535,0.0002770044,0.0001929774,0.000217992,0.4186761,0.007503536,0.0497816,0.008961059,0.5086603],"study_design_scores_gemma":[0.00001644737,0.0000194537,0.0003191636,0.00000978757,0.00001295179,0.00006348261,0.00001023166,0.9877371,0.00122696,0.009197979,0.001370403,0.00001605736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002651686,0.0001108228,0.9962752,0.00008213928,0.00001350468,0.00003229493,0.00005701811,0.0004416344,0.0003358168],"genre_scores_gemma":[0.2181935,0.0004656822,0.7707059,0.0004813738,0.0001119925,0.0007205961,0.0017201,0.0005495592,0.007051256],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00768509,"threshold_uncertainty_score":0.01528072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05442390156610317,"score_gpt":0.2909666643421822,"score_spread":0.236542762776079,"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."}}