{"id":"W4313564281","doi":"10.1109/icit48603.2022.10002752","title":"Bayesian Model and Feature Selection in Asymmetric Generalized Gaussian Mixtures","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":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Feature selection; Computer science; Gaussian process; Bayesian probability; Gaussian; Artificial intelligence; Selection (genetic algorithm); Model selection; Pattern recognition (psychology); Feature (linguistics); Machine learning; Algorithm; Chemistry","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"],"consensus_categories":[],"category_scores_codex":[0.0006427706,0.0002774693,0.0003378111,0.001991896,0.0002707803,0.0001432383,0.001421958,0.0004533259,0.0001161128],"category_scores_gemma":[0.0001363298,0.0002824234,0.00007183529,0.001878295,0.00009201936,0.0002427633,0.0004497844,0.001833485,0.00000353058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000318775,"about_ca_system_score_gemma":0.0002630132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006489953,"about_ca_topic_score_gemma":0.00004388936,"domain_scores_codex":[0.9976048,0.0002682842,0.0003731953,0.0008115387,0.000552537,0.0003896849],"domain_scores_gemma":[0.9992133,0.00006863732,0.0002157948,0.0003218158,0.00009551281,0.00008495514],"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":[0.0001132017,0.0001322922,0.0006696174,0.0000022249,0.00004850664,0.00004557716,0.0001213003,0.001529093,0.007869242,0.8783273,0.005372485,0.1057692],"study_design_scores_gemma":[0.001920233,0.0003386628,0.00008561416,0.00002414348,0.00001055355,0.0001184337,0.00005408855,0.7137913,0.004493034,0.2767716,0.001993371,0.000398995],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0320428,0.0001120535,0.9309235,0.02545792,0.001796203,0.000523256,0.00005043275,0.0003431943,0.008750624],"genre_scores_gemma":[0.9548545,0.00006214404,0.04229923,0.0007519894,0.0001785506,0.0002288759,0.00001358953,0.00002017859,0.001590864],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9228117,"threshold_uncertainty_score":0.9999628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06199279767613432,"score_gpt":0.3106579513614001,"score_spread":0.2486651536852658,"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."}}