{"id":"W2792781130","doi":"10.1007/s10548-018-0627-x","title":"Localizing Event-Related Potentials Using Multi-source Minimum Variance Beamformers: A Validation Study","year":2018,"lang":"en","type":"article","venue":"Brain Topography","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; University of British Columbia","keywords":"Variance (accounting); Event (particle physics); Event-related potential; Variance components; Computer science; Artificial intelligence; Statistics; Pattern recognition (psychology); Electroencephalography; Psychology; Mathematics; Neuroscience; Physics; Accounting","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008154013,0.0002350541,0.0002377113,0.0004095488,0.0005331551,0.0004343562,0.0007292318,0.00009849741,0.00002508999],"category_scores_gemma":[0.0001045741,0.0002319638,0.0001522292,0.001923099,0.0001319138,0.0009660331,0.0002106604,0.0001447323,0.00003532556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003126839,"about_ca_system_score_gemma":0.00007257516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000645314,"about_ca_topic_score_gemma":0.00001353073,"domain_scores_codex":[0.9978069,0.0001852703,0.0004608835,0.0006519146,0.0004000771,0.0004949194],"domain_scores_gemma":[0.9987921,0.00007610353,0.0002806506,0.0005429003,0.0001742965,0.000133951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001073212,0.003791892,0.08677788,0.0001621407,0.001012903,0.0001424501,0.03349661,0.00259965,0.4180048,0.0008755267,0.001968555,0.4510602],"study_design_scores_gemma":[0.01362457,0.003000719,0.04386077,0.001114372,0.0003660458,0.000335444,0.006938987,0.3809719,0.5225771,0.009606643,0.01328765,0.004315782],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3556204,0.0001351489,0.6430323,0.0002311462,0.0003856435,0.0002754047,7.765294e-7,0.0002064394,0.0001127433],"genre_scores_gemma":[0.8728558,0.000001954436,0.1263125,0.0005170699,0.0001467395,0.000009725069,0.000002853211,0.00002084049,0.0001324396],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5172355,"threshold_uncertainty_score":0.9459211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02536997575659763,"score_gpt":0.2985467300626796,"score_spread":0.273176754306082,"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."}}