{"id":"W1995209220","doi":"10.1007/s11517-009-0504-6","title":"The optimal linear transformation-based fMRI feature space analysis","year":2009,"lang":"en","type":"article","venue":"Medical & Biological Engineering & Computing","topic":"Blind Source Separation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Functional magnetic resonance imaging; Artificial intelligence; Pattern recognition (psychology); Feature vector; Computer science; Signature (topology); Cluster analysis; Transformation (genetics); Feature (linguistics); Computer vision; Feature extraction; Mathematics; Neuroscience; Psychology","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":[0.001297382,0.0001905153,0.0002623242,0.0001261289,0.0002304243,0.0001528787,0.001009688,0.0002389362,0.00001543151],"category_scores_gemma":[0.0004595278,0.0001169851,0.0002078068,0.001149015,0.00005379873,0.00009676473,0.00007628541,0.0006044155,0.00001414049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003438491,"about_ca_system_score_gemma":0.00005431981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003649387,"about_ca_topic_score_gemma":7.458404e-7,"domain_scores_codex":[0.998376,0.0001100585,0.000325112,0.0003216806,0.0004755144,0.0003916183],"domain_scores_gemma":[0.9986714,0.0006472096,0.0000731786,0.0003384366,0.00006642562,0.0002034028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001712333,0.0001509751,0.0006430615,0.00001195089,0.000154304,0.00003383084,0.0007709531,0.7780026,0.0005040898,0.1105871,0.000721058,0.1084029],"study_design_scores_gemma":[0.0001320322,0.0001092002,0.006469645,0.0000181087,0.00001110806,0.000005025279,0.000006422507,0.9857528,0.0004806792,0.00007589343,0.006778987,0.0001600635],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01946614,0.0001439079,0.9617033,0.01735759,0.00008923464,0.0001178295,5.984571e-7,0.0007984026,0.0003229794],"genre_scores_gemma":[0.879696,0.00001271474,0.118745,0.001418657,0.00009746931,0.00000338112,0.00001006155,0.000004090807,0.00001268323],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8602298,"threshold_uncertainty_score":0.4770512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009585005801947604,"score_gpt":0.2563440809671029,"score_spread":0.2467590751651553,"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."}}