{"id":"W1525328938","doi":"10.1002/hbm.22490","title":"Comparing within‐subject classification and regularization methods in fMRI for large and small sample sizes","year":2014,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"Canadian Institutes of Health Research; Heart and Stroke Foundation of Canada; James S. McDonnell Foundation","keywords":"Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Principal component analysis; Computer science; Quadratic classifier; Support vector machine; Sample size determination; Covariance; Mathematics; Statistics","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":[0.0662481,0.001350806,0.001564769,0.001409931,0.0008972188,0.001493859,0.00171035,0.002252768,0.001235861],"category_scores_gemma":[0.1346506,0.0007964673,0.002011889,0.001237025,0.002399566,0.002591546,0.001626489,0.00206016,0.0005124212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001216342,"about_ca_system_score_gemma":0.001498007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002400368,"about_ca_topic_score_gemma":0.002978663,"domain_scores_codex":[0.9735872,0.01837585,0.001058556,0.003544591,0.003063764,0.0003700756],"domain_scores_gemma":[0.8903641,0.0889954,0.003854421,0.01114352,0.005167276,0.0004752889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005359874,0.0007908266,0.03381417,0.001733202,0.005170066,0.0002913479,0.002223063,0.2548779,0.04446617,0.0196966,0.007163311,0.6244134],"study_design_scores_gemma":[0.0005052553,0.001548212,0.04490598,0.0003104149,0.0007961505,0.0004339962,0.0002285296,0.8752334,0.02639894,0.04173115,0.007589607,0.0003184175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1445318,0.004213887,0.8441309,0.001486111,0.0003749733,0.0006007185,0.0002581988,0.001938768,0.00246474],"genre_scores_gemma":[0.5068091,0.001284274,0.4846985,0.0006568811,0.0002412946,0.001941428,0.0006080835,0.001715405,0.002045008],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0662481,"threshold_uncertainty_score":0.3503577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1584137255291783,"score_gpt":0.3461396521158779,"score_spread":0.1877259265866996,"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."}}