{"id":"W4390493794","doi":"10.1109/snams60348.2023.10375396","title":"Adaptive Constrained ICAMGGMM: An Improvement Over ICA","year":2023,"lang":"en","type":"article","venue":"","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Independent component analysis; Robustness (evolution); Computer science; Blind signal separation; Covariance matrix; Multivariate statistics; Covariance; Source separation; Pattern recognition (psychology); Gaussian; Algorithm; Artificial intelligence; Mathematics; Machine learning; Statistics","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":[0.0003284512,0.00008931561,0.00008744968,0.0001178628,0.00006398264,0.0001179694,0.0004758457,0.00004667699,0.0001034837],"category_scores_gemma":[0.00001306323,0.00007737803,0.00003608907,0.0004365353,0.00003971999,0.0004888301,0.0001928146,0.00007928413,0.0001798985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002152902,"about_ca_system_score_gemma":0.00006662603,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003891718,"about_ca_topic_score_gemma":0.00001844795,"domain_scores_codex":[0.9990961,0.00004355137,0.000150456,0.0002945822,0.000221416,0.0001938693],"domain_scores_gemma":[0.999327,0.00004522562,0.00004129243,0.0004435965,0.00005488935,0.00008801596],"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":[0.000003616813,0.0000559449,0.0000371815,0.000001712547,0.00001189112,0.000009067606,0.001242594,0.00002306401,0.008312494,0.9258553,0.01174648,0.05270065],"study_design_scores_gemma":[0.001087123,0.001967788,0.007225673,0.00001698372,0.000007783598,0.000009766002,0.0007307759,0.7445991,0.1090237,0.110707,0.0237762,0.0008481455],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02182793,0.000002353363,0.9332404,0.002010492,0.00009939999,0.0002762573,0.000003020232,0.002939094,0.03960103],"genre_scores_gemma":[0.9278977,0.000003505794,0.06776671,0.002255905,0.00002665851,0.00003633868,0.000005191419,0.000007072354,0.002000949],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9060698,"threshold_uncertainty_score":0.3155384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02926304484837247,"score_gpt":0.2956137511194639,"score_spread":0.2663507062710915,"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."}}