{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0005233867,0.0004048257,0.0003989194,0.0009539622,0.0005171521,0.0002066752,0.00274734,0.0005917371,0.0003767078],"category_scores_gemma":[0.00005657842,0.0003738431,0.0001176924,0.001478091,0.0001459685,0.0003722679,0.0005208683,0.002836487,0.00001655689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003898463,"about_ca_system_score_gemma":0.0006500736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002378556,"about_ca_topic_score_gemma":0.00003280499,"domain_scores_codex":[0.9968739,0.000216767,0.0003862431,0.001058404,0.0009479246,0.0005167314],"domain_scores_gemma":[0.9984841,0.00006490351,0.0003437724,0.0006907066,0.0002966433,0.0001199076],"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":[0.0002602839,0.0002029965,0.0003137031,0.00000310513,0.0001538799,0.00007236547,0.000155544,0.002641687,0.005436953,0.9097734,0.02208499,0.05890111],"study_design_scores_gemma":[0.003056343,0.001847898,0.00002732246,0.00007851889,0.0000550825,0.000493031,0.0001789478,0.7414125,0.01439609,0.2146786,0.02270995,0.001065736],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006595575,0.00002596041,0.9400232,0.03965907,0.001812895,0.0004797448,0.00008044136,0.0006109861,0.01071213],"genre_scores_gemma":[0.8957037,0.00002031447,0.09374267,0.00154209,0.000492249,0.0003999526,0.00004938075,0.00005030244,0.007999363],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8891081,"threshold_uncertainty_score":0.9998714,"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."}}