{"id":"W2086960512","doi":"10.1371/journal.pone.0118877","title":"How Many Separable Sources? Model Selection In Independent Components Analysis","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; James S. McDonnell Foundation; National Institutes of Health; National Center for Research Resources; Brain Mapping Support Foundation; Ahmanson Foundation","keywords":"Selection (genetic algorithm); Model selection; Separable space; Computer science; Computational biology; Biology; Mathematics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01234008,0.001548514,0.002177548,0.002143472,0.0009301726,0.003056102,0.002127145,0.00264784,0.002077274],"category_scores_gemma":[0.03943519,0.001157728,0.001591557,0.002883448,0.003228388,0.005189969,0.002386469,0.003800865,0.001219346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001245005,"about_ca_system_score_gemma":0.001843803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003287886,"about_ca_topic_score_gemma":0.002381198,"domain_scores_codex":[0.9922692,0.004681083,0.0002650324,0.001248617,0.001350991,0.0001850865],"domain_scores_gemma":[0.9889498,0.008979779,0.0004972469,0.0006577263,0.0007580544,0.0001573899],"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":[0.000256085,0.0001374564,0.0041743,0.0008181812,0.0006011451,0.000407806,0.0008578041,0.2824361,0.00224329,0.3568826,0.01094051,0.3402447],"study_design_scores_gemma":[0.00005109937,0.00005372416,0.0008455783,0.0001881134,0.0000760029,0.0001922618,0.0001747978,0.4582548,0.000962718,0.5324675,0.006662276,0.00007127076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00386415,0.001490068,0.9912696,0.001854331,0.00007237407,0.00005078877,0.00006645054,0.0001251885,0.001207148],"genre_scores_gemma":[0.2166415,0.004439764,0.7721393,0.001239441,0.0007219969,0.0006227939,0.0005607733,0.0002253976,0.003409025],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01234008,"threshold_uncertainty_score":0.06526136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1144038770537468,"score_gpt":0.259953734054026,"score_spread":0.1455498570002792,"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."}}